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Journal of Biomedicine and Biotechnology
Volume 2005 (2005), Issue 2, Pages 155-159
doi:10.1155/JBB.2005.155
Research article

Gene Expression Data Classification With Kernel Principal Component Analysis

Zhenqiu Liu,1 Dechang Chen,2 and Halima Bensmail3

1Bioinformatics Cell, US Army Medical Research and Materiel Command, 110 North Market Street, Frederick 21703, MD, USA
2Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda 20814, MD, USA
3Department of Statistics, University of Tennessee, 331 Stokely Management Center, Knoxville 37996, TN, USA

Received 3 June 2004; Revised 28 August 2004; Accepted 3 September 2004

Copyright © 2005 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N<M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.