Bioinformatics Vol. 19 no. 1 2003
Pages 37-44
© 2003 Oxford University Press
Genetic algorithms applied to multi-class prediction for the analysis of gene expression data
1 Nanyang Technological University,
School of Mechanical and Production Engineering, 50 Nanyang Avenue,
Singapore 639798
2 Division of Cellular and Molecular Research,
National Cancer Center/Defence Medical Research Institute,
11 Hospital Drive, Singapore 169610, Republic of Singapore
Received on April 17, 2002
; revised on July 3, 2002
; accepted on July 15, 2002
Motivation: An important challenge in the use of large-scale gene expression data for biological classification occurs when the expression dataset being analyzed involves multiple classes. Key issues that need to be addressed under such circumstances are the efficient selection of good predictive gene groups from datasets that are inherently ‘noisy’, and the development of new methodologies that can enhance the successful classification of these complex datasets.
Methods: We have applied genetic algorithms (GAs) to the problem of multi-class prediction. A GA-based gene selection scheme is described that automatically determines the members of a predictive gene group, as well as the optimal group size, that maximizes classification success using a maximum likelihood (MLHD) classification method.
Results: The GA/MLHD-based approach achieves higher classification accuracies than other published predictive methods on the same multi-class test dataset. It also permits substantial feature reduction in classifier genesets without compromising predictive accuracy. We propose that GA-based algorithms may represent a powerful new tool in the analysis and exploration of complex multi-class gene expression data.
Availability: Supplementary information, data sets and source codes are available at http://www.omniarray.com/bioinformatics/GA
Contact: cmrtan{at}nccs.com.sg
* To whom correspondence should be addressed.
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