Informative gene for cancer classification by using particle swarm optimization

M. S. Mohamad, S. Omatu, S. Deris, M. Yoshioka

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

Abstract

Microarrays technology offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Gene expression data that produced by the technology are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contributes to a cancer disease. Therefore, this article proposes particle swarm optimization (PSO) with the constraint of particle's velocities to select a near-optimal (small) subset of informative genes that is relevant for cancer classification. The performance of the proposed method was evaluated by two well-known gene expression data sets and obtained encouraging results as compared with the standard version of binary PSO.

Original languageEnglish
Title of host publicationProceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10
Pages650-653
Number of pages4
Publication statusPublished - 2010
Externally publishedYes
Event15th International Symposium on Artificial Life and Robotics, AROB '10 - Beppu, Oita, Japan
Duration: Feb 4 2010Feb 6 2010

Publication series

NameProceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10

Conference

Conference15th International Symposium on Artificial Life and Robotics, AROB '10
Country/TerritoryJapan
CityBeppu, Oita
Period2/4/102/6/10

Keywords

  • Binary particle swarm optimization
  • Gene expression data
  • Gene selection
  • Optimization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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