Gene subset selection using an iterative approach based on genetic algorithms

Mohd Saberi Mohamad, Sigeru Omatu, Safaai Deris, Michifumi Yoshioka

Research output: Contribution to journalArticlepeer-review


Microarray data are expected to be useful for cancer classification. However, the process of gene selection for the classification contains a major problem due to properties of the data such as the small number of samples compared with the huge number of genes (higher-dimensional data), irrelevant genes, and noisy data. Hence, this article aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve this aim, an iterative approach based on genetic algorithms has been proposed. Experimental results show that the performance of the proposed approach is superior to other previous related work, as well as to four methods tried in this work. In addition, a list of informative genes in the best gene subsets is also presented for biological usage.

Original languageEnglish
Pages (from-to)12-15
Number of pages4
JournalArtificial Life and Robotics
Issue number1
Publication statusPublished - 2009
Externally publishedYes


  • Gene selection
  • Genetic algorithm
  • Iterative approach
  • Microarray data

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • Artificial Intelligence


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