Selecting Informative Genes from Leukemia Gene Expression Data using a Hybrid Approach for Cancer Classification

  • Mohd Saberi Mohamad
  • Safaai Deris
  • Siti Zaiton
  • Mohd Hashim
Conference paper

DOI: 10.1007/978-3-540-68017-8_133

Part of the IFMBE Proceedings book series (IFMBE, volume 15)
Cite this paper as:
Mohamad M.S., Deris S., Zaiton S., Hashim M. (2007) Selecting Informative Genes from Leukemia Gene Expression Data using a Hybrid Approach for Cancer Classification. In: Ibrahim F., Osman N.A.A., Usman J., Kadri N.A. (eds) 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. IFMBE Proceedings, vol 15. Springer, Berlin, Heidelberg

Abstract

The development of microarray-based high-throughput gene profiling has led to the hope that this technology could provide an efficient and accurate means of diagnosing and classifying cancers. However, the large amount of data generated by microarrays requires effective selection of informative genes for cancer classification. Key issue that needs to be addressed is a selection of small number of informative genes that contribute to a disease from the thousands of genes measured on microarrays. This work deals with finding the small subset of informative genes from gene expression microarray data which maximize the classification accuracy. We introduce an improved version of hybrid of genetic algorithm and support vector machine for genes selection and classification. We show that the classification accuracy of the proposed approach is superior to a number of current state-of-the-art methods of one widely used benchmark dataset. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biology and computer scientist researchers in order to explore the biological plausibility.

Keywords

Gene selection classification genetic algorithm support vector machine gene expression microarray 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Mohd Saberi Mohamad
    • 2
    • 1
  • Safaai Deris
    • 2
  • Siti Zaiton
    • 2
  • Mohd Hashim
    • 2
  1. 1.Universiti Teknologi MalaysiaJohor Bharu, JohorMalaysia
  2. 2.Laboratory of Artificial Intelligence and Bioinformatics, Software Engineering Department, Faculty of Computer Science and Information SystemsUniversiti Teknologi MalaysiaJohor Bharu, JohorMalaysia

Personalised recommendations