Applying genetic algorithms and support vector machines to the gene selection problem

Abstract

Microarrays are emerging technologies that allow biologists to better understand the interactions between several pathologic states, at genes level. However, the amount of data generated by these tools becomes problematic when data are supposed to be automatically analyzed (e.g. for diagnostic purposes). In this work, the authors present a novel gene selection method based on Genetic Algorithms and Support Vector Machines (SVMs) for the classification of tissue samples. For such, the authors use an error estimate for SVMs to evaluate each individual's fitness. The proposed method is compared with common used gene selection techniques. Experimental results carried out using public available microarray datasets demonstrated the strength of the approach.