Gene Expression Data Classification with Revised Kernel Partial Least Squares Algorithm

Zhenqiu Liu and Dechang Chen

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 partial least squares (KPLS) and classification with logistic regression (discrimination) and other standard machine learning methods. KPLS is a generalization and nonlinear version of partial least squares (PLS). 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.

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