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

  • Authors:
  • Bruno Feres de Souza;André/ C.P.L.F. Carvalho;Waldo Cancino Ticona

  • Affiliations:
  • (Correspd. T.: +55 16 3373 9646/ bferes@icmc.usp.br) Univ. de Sã/o Paulo, Insto. de Ciê/ncias Matemá/ticas e de Computaç/ã/o, Av. Trabalhador Sã/o-Carlense, 400, Mailbox 66 ...;Univ. de Sã/o Paulo, Instituto de Ciê/ncias Matemá/ticas e de Computaç/ã/o, Av. Trabalhador Sã/o-Carlense, 400, Mailbox 668. CEP 13560-970, Sã/o Carlos - SP, Brasil;Universidade de Sã/o Paulo, Instituto de Ciê/ncias Matemá/ticas e de Computaç/ã/o, Av. Trabalhador Sã/o-Carlense, 400, Mailbox 668. CEP 13560-970, Sã/o Carlos - SP, Bra ...

  • Venue:
  • Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - VIII Brazilian Symposium on Neural Networks
  • Year:
  • 2007

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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.