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

M. S. Mohamad, S. Omatu, S. Deris, M. F. Misman

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

Abstract

The development of microarray-based high-throughput gene expression has led to the hope that this technology could provide an efficient cancer diagnosis and classification platform. A major problem in these gene expression data is that the number of genes greatly exceeds the number of tissue samples. Moreover, these data have a noisy nature. It has been shown that selecting a small subset of informative genes can lead to improved classification accuracy. Thus, this paper aims to select a small subset of informative genes that are most relevant for the classification task. To achieve this aim, a hybrid algorithm that combines two hybrid methods has been developed. This algorithm is assessed on two well-known microarray data sets, showing competitive results.

Original languageEnglish
Title of host publicationProceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08
Pages328-331
Number of pages4
Publication statusPublished - 2008
Externally publishedYes
Event13th International Symposium on Artificial Life and Robotics, AROB 13th'08 - Oita, Japan
Duration: Jan 31 2008Feb 2 2008

Publication series

NameProceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08

Conference

Conference13th International Symposium on Artificial Life and Robotics, AROB 13th'08
Country/TerritoryJapan
CityOita
Period1/31/082/2/08

Keywords

  • Cancer classification
  • Gene selection
  • Hybrid algorithm
  • Microarray data

ASJC Scopus subject areas

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

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