Gene Expression Data Modeling and Validation of Gene Selection Methods

Abstract

Several gene selection methods have been proposed to identify sets of genes related to a particular disease or to a particular functional status of the tissue. An open problem with gene selection methods consists in evaluating their performance; since we usually know only a smell subset of the genes involved in the onset of a status, and many times no relevant genes are known “a priori”. We propose an artificial system, based on modeling gene expression signatures, to generate synthetic gene expression data for validating gene selection methods. Comparison between gene selection methods using data generated through the artificial model are performed and preliminary results are reported.