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

Issue title: VIII Brazilian Symposium on Neural Networks

Article type: Research Article

Authors: de Souza, Bruno Feres | Carvalho, André C.P.L.F. | Ticona, Waldo Cancino

Affiliations: Universidade de São Paulo, Instituto de Ciências Matemáticas e de Computação, Av. Trabalhador São-Carlense, 400, Mailbox 668. CEP 13560–970, São Carlos – SP, Brasil

Note: [] Corresponding author. Tel.: +55 16 3373 9646; E-mail: bferes@icmc.usp.br

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.

Keywords: Support vector machines, genetic algorithms, gene selection, microarrays

Journal: Journal of Intelligent & Fuzzy Systems, vol. 18, no. 5, pp. 435-444, 2007