Selecting a smaller subset of informative genes from microarray data via a three-stage method

Abstract

Microarray data produced by microarray are useful for cancer classification. However, the process of gene selection for the 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 (higher-dimensional data), irrelevant genes, and noisy data. Hence, this paper proposes a three-stage gene selection method to select a smaller subset of informative genes that is most relevant for the cancer classification. It has three stages: I) pre-selecting genes using a filter method to produce a subset of genes; 2) optimising the gene subset using a multi-objective hybrid method to yield near-optimal subsets of genes; 3) analysing the frequency of appearance of each gene in the different near-optimal gene subsets to produce a smaller (final) subset of informative genes. Two microarray data sets are used to test the effectiveness of the proposed method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works. A list of informative genes in the final gene subset is also presented for biological usage.