Selecting informative genes from microarray data by using hybrid methods for cancer classification

Mohd Saberi Mohamad, Sigeru Omatu, Safaai Deris, Muhammad Faiz Misman, Michifumi Yoshioka

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results.

Original languageEnglish
Pages (from-to)414-417
Number of pages4
JournalArtificial Life and Robotics
Issue number2
Publication statusPublished - 2009
Externally publishedYes


  • Cancer classification
  • Gene selection
  • Geneti calgorithm
  • Hybrid method
  • Microarray data

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • Artificial Intelligence


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