Introduction As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically-relevant endpoints. Methods We used gene expression data from 230 breast cancers (grouped into training and independent validation sets)

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BACKGROUND: Gene expression analysis has emerged as a major biological research area, with real-time quantitative reverse transcription PCR (RT-QPCR) being one of the most accurate and widely used techniques for expression profiling of selected genes. In order to obtain results that are comparable across assays, a stable normalization strategy is required. In general, the normalization of PCR measurements between different samples uses one to several