Gene Expression Data Classification With Kernel Principal Component Analysis
Open Access
- 1 January 2005
- journal article
- research article
- Published by Hindawi Limited in Journal of Biomedicine and Biotechnology
- Vol. 2005 (2), 155-159
- https://doi.org/10.1155/jbb.2005.155
Abstract
One important feature of the gene expression data is that the number of genesfar exceeds the number of samples. Standard statistical methods do not work well when. 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.
Keywords
Funding Information
- National Science Foundation (CCR-0311252)
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