MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.
MiRNA expression intensity values were background corrected and normalized across arrays using the Robust Multichip Average (RMA) method. The intensity data used in all analysis were log (2) transformed. For each array the miRNA probe set signals were compared to the distribution of signals for anti-genomic probes that had matching GC content (miRNA QC Tool, Version 1.0.33.0) and following the manufacture’s recommendation, Wilcoxon Rank-Sum test of  p<0.06 was used to identify miRNAs above background. Subsequent analysis was restricted to 414 miRNAs that exceeded background levels in at least 50 women. Conditional logistic regression was used to identify differentially expressed miRNA probes between cases and controls for those 414 probes.