Learning diagnostic signatures from microarray data using L1-regularized logistic regression

Making reliable diagnoses and predictions based on high-throughput transcriptional data has attracted immense attention in the past few years. While experimental gene profiling techniques—such as microarray platforms—are advancing rapidly, there is an increasing demand of computational methods being able to efficiently handle such data. In this work we propose a computational workflow for extracting diagnostic gene signatures from high-throughput transcriptional profiling data. In particular, our research was performed within the scope of the first IMPROVER challenge. The goal of that challenge was to extract and verify diagnostic signatures based on microarray gene expression data in four different disease areas: psoriasis, multiple sclerosis, chronic obstructive pulmonary disease and lung cancer. Each of the different disease areas is handled using the same three-stage algorithm. First, the data are normalized based on a multi-array average (RMA) normalization procedure to account for variability among ...