A recursive genetic algorithm to automatically select genes for cancer classification

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Gene expression technology namely microarray, offers the ability to measure the expression levels of thousands of genes simultaneously in a biological organism. Microarray data are expected to be of significant help in the development of efficient cancer diagnosis and classification platform. The main problem that needs to be addressed is the selection of a small subset of genes that contributes to a disease from the thousands of genes measured on microarray that are inherently noisy. Most approaches from previous works have selected the numbers of genes manually and thus, have caused difficulty, especially for beginner biologists. Hence, this paper aims to automatically select a small subset of informative genes that is most relevant for the cancer classification. In order to achieve this aim, a recursive genetic algorithm has been proposed. Experimental results show that the gene subset is small in size and yield better classification accuracy as compared with other previous works as well as four methods experimented in this work. A list of informative genes in the best subsets is also presented for biological usage.

Original languageEnglish
Title of host publication2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008)
EditorsJuan Corchado, Juan De Paz, Miguel Rocha, Florentino Fernandez Riverola
Number of pages9
Publication statusPublished - 2009
Externally publishedYes

Publication series

NameAdvances in Soft Computing
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794


  • Cancer classification
  • Gene selection
  • Genetic Algorithm
  • Microarray data
  • Recursive genetic algorithm
  • Support vector machine

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mechanics
  • Computer Science Applications


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