A Three-Stage Method to Select Informative Genes from Gene Expression Data in Classifying Cancer Classes

The process of gene selection for the cancer classification faces with a major problem due to the properties of the data such as the small number of samples compared to the huge number of genes, irrelevant genes, and noisy data. Hence, this paper aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve the aim, a three-stage method has been proposed. It has three stages: 1) pre-selecting genes using a filter method; 2) optimizing the gene subset using a multi-objective hybrid method; 3) analyzing the frequency of appearance of each gene. By performing experiments on three public gene expression data sets, classification accuracies and the number of selected genes of the proposed method are better than those of other experimented methods and previous works. A list of informative genes in the final gene subsets is also presented for biological usage.