Integrated use of statistical-based approaches and computational intelligence techniques for tumors classification using Microarray

Abstract

With the recent development of biotechnologies, cDNA microarray chips are increasingly applied in cancer research. Microarray experiments can lead to a more thorough grasp of the molecular variations among tumors because they can allow the monitoring of expression levels in cells for thousands of genes simultaneously. Accordingly, how to successfully discriminate the classes of tumors using gene expression data is an urgent research issue and plays an important role in carcinogenesis. To refine the large dimension of the genes data and effectively classify tumor classes, this study proposes several hybrid discrimination procedures that combine the statistical-based techniques and computational intelligence approaches to discriminate the tumor classes. A real microarray data set was used to demonstrate the performance of the proposed approaches. In addition, the results of cross-validation experiments reveal that the proposed two-stage hybrid models are more efficient in discriminating the acute leukemia classes than the established single stage models.