A Novel Hybrid Method for Gene Selection of Microarray Data

Nowadays, biologists, using microarray technologies, can analysis thousands of gene expression values at one time. So they can have an overall understanding of the cell. However, gene expression profiles commonly have thousands of gene expression values but only limited tissue samples, for some reasons. The irrelevant gene expression data not only increases computational complexity but also makes the discovery of relevant genes impossible. Therefore, we present a new hybrid method for gene selection to solve this problem. In this paper, we first use filter method to rank the genes in terms of their expression difference, and then select 'important' genes with high 'score'. Afterwards, an ant colony is used in clustering gene expression data. A support vector machine is applied to validate the classification performance of candidate genes. The experimental results demonstrate the effectiveness of our method in addressing the problem.