Evolving artificial neural networks for DNA microarray analysis

DNA microarray technology provides a format for the simultaneous measurement of the expression level of thousands of genes in a single hybridization assay. One exciting result of microarray technology has been the demonstration that patterns of gene expression can distinguish between tumors of different anatomical origins. Standard statistical methodologies in classification and prediction do not work well or even at all when N (the number of samples) < p (genes). Modification of existing statistical methodologies or development of new methodologies are needed for the analysis of cancer. Recently, designing artificial neural networks (ANNs) by evolutionary algorithms has emerged as a preferred alternative to the common practice of selecting the apparent best network. We propose an evolutionary neural network that classifies gene expression profiles into normal or colon cancer cell. Colon cancer is the second only to lung cancer as a cause of cancer-related mortality in Western countries. Colon cancer is a genetic disease, propagated by the acquisition of somatic alterations that influence gene expression. Experimental results on colon microarray data with evolutionary neural network show that the proposed method can perform better than other classifiers. Contribution of this article is applying evolutionary neural network to gene expression classification problem.