Data Mining in Genomics and ProteomicsView this Special Issue
Research article | Open Access
Zhenqiu Liu, Dechang Chen, Halima Bensmail, "Gene Expression Data Classification With Kernel Principal Component Analysis", BioMed Research International, vol. 2005, Article ID 905863, 5 pages, 2005. https://doi.org/10.1155/JBB.2005.155
Gene Expression Data Classification With Kernel Principal Component Analysis
One important feature of the gene expression data is that the number of genes far 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.
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.