Abstract
Microarray experiments are emerging as one of the main driving forces in modern biology. By allowing the simultaneous monitoring of the expression of the entire genome for a given organism, array experiments provide tremendous insight into the fundamental biological processes that translate genetic information. One of the major challenges is to identify computationally efficient and biologically meaningful analysis approaches to extract the most informative and unbiased components of the microarray data. In this paper we introduce a framework that integrates machine learning and optimization techniques for the selection of maximally informative genes in microarray expression experiments. The proposed approach provides tremendous reduction in the number of informative genes, compared to similar analyses by generating biologically relevant minimal subsets of genes.