Gene Selection Using Wilcoxon Rank Sum Test and Support Vector Machine for Cancer Classification

Authors:
Chen Liao;Shutao Li;Zhiyuan Luo
Affiliations:
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;Department of Computer Science, Royal Holloway College, University of London, Egham, Surrey, TW20 0EX, United Kingdom
Venue:
Computational Intelligence and Security
Year:
2007

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Abstract

Gene selection is an important problem in microarray data processing. A new gene selection method based on Wilcoxon rank sum test and Support Vector Machine (SVM) is proposed in this paper. First, Wilcoxon rank sum test is used to select a subset. Then each selected gene is trained and tested using SVM classifier with linear kernel separately, and genes with high testing accuracy rates are chosen to form the final reduced gene subset. Leave-one-out cross validation (LOOCV) classification results on two datasets: Breast Cancer and ALL/AML leukemia, demonstrate the proposed method can get 100% success rate with the final reduced subset. The selected genes are listed and their expression levels are sketched, which show that the selected genes can make clear separation between two classes.