Selection of discriminative genes in microarray experiments using mathematical programming

Microarray technologies allow the measurement of thousands of gene expression levels simultaneously. With them biologists can have a powerful new tool to analyse the complex dynamical process of living organisms. These technologies are challenging traditional scientific disciplines, including Computer Science and Statistics. The reason of this challenge is based on the novel type of large-scale data mining applications. A typical microarray experiment is very costly and, due to budget limitations, the ratio of experiments to genes is generally on the order of 1/100. As a consequence, we rely on combinatorial optimization formalisms to develop robust feature selection methods. In this paper we demonstrate their usefulness in selecting genes that allow a molecular classification of cancer samples when we are given as labels their assumed origin (Colon, Melanoma, etc.). We present some results on five types of cancer presented on a public domain dataset which will allow for the reproducibility of our results.