Support vector machine classifications for microarray expression data set

Abstract

Gene selection, cancer classification and functional gene classification are three main concerns and interests by biologists for cancer detection, cancer classification, and understanding the functions of genes from the molecular level of tissues, where the large number of genes and relatively small number of experiments in gene expression data generate a great challenge. After a brief introduction of support vector machine(SVM) for classification, this paper presents recent SVM approaches for gene selection, cancer classification and functional gene classification followed by analysis on the advantages and limitations of SVM on these applications.