A three-stage method to select informative genes from gene expression data in classifying cancer classes

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

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

2 Citations (Scopus)

Abstract

The process of gene selection for the cancer classification faces with a major problem due to the properties of the data such as the small number of samples compared to the huge number of genes, irrelevant genes, and noisy data. Hence, this paper aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve the aim, a three-stage method has been proposed. It has three stages: 1) pre-selecting genes using a filter method; 2) optimizing the gene subset using a multi-objective hybrid method; 3) analyzing the frequency of appearance of each gene. By performing experiments on three public gene expression data sets, classification accuracies and the number of selected genes of the proposed method are better than those of other experimented methods and previous works. A list of informative genes in the final gene subsets is also presented for biological usage.

Original languageEnglish
Title of host publicationISMS 2010 - UKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation
Pages158-163
Number of pages6
DOIs
Publication statusPublished - 2010
Externally publishedYes
EventUKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2010 - Liverpool, United Kingdom
Duration: Jan 27 2010Jan 29 2010

Publication series

NameISMS 2010 - UKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation

Conference

ConferenceUKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2010
Country/TerritoryUnited Kingdom
CityLiverpool
Period1/27/101/29/10

Keywords

  • Cancer classification
  • Component
  • Gene expression data
  • Gene selection
  • Genetic algorithm
  • Three-stage method

ASJC Scopus subject areas

  • General Computer Science
  • Theoretical Computer Science

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