Artificial Life and Robotics

, Volume 11, Issue 2, pp 219–222

A model for gene selection and classification of gene expression data

  • Mohd Saberi Mohamad
  • Sigeru Omatu
  • Safaai Deris
  • Siti Zaiton Mohd Hashim
ORIGINAL ARTICLE

DOI: 10.1007/s10015-007-0432-1

Cite this article as:
Mohamad, M., Omatu, S., Deris, S. et al. Artif Life Robotics (2007) 11: 219. doi:10.1007/s10015-007-0432-1

Abstract

Gene expression data are expected to be of significant help in the development of efficient cancer diagnosis and classification platforms. One problem arising from these data is how to select a small subset of genes from thousands of genes and a few samples that are inherently noisy. This research aims to select a small subset of informative genes from the gene expression data which will maximize the classification accuracy. A model for gene selection and classification has been developed by using a filter approach, and an improved hybrid of the genetic algorithm and a support vector machine classifier. We show that the classification accuracy of the proposed model is useful for the cancer classification of one widely used gene expression benchmark data set.

Key words

Gene selectionHybrid approachFilter approachGene expression data

Copyright information

© ISAROB 2007

Authors and Affiliations

  • Mohd Saberi Mohamad
    • 1
  • Sigeru Omatu
    • 2
  • Safaai Deris
    • 1
  • Siti Zaiton Mohd Hashim
    • 1
  1. 1.Department of Software Engineering, Faculty of Computer Science and Information SystemsUniversiti Teknologi MalaysiaJohoreMalaysia
  2. 2.Department of Computer Science and Intelligent SystemsGraduate School of Engineering, Osaka Prefecture UniversityOsakaJapan