Colon cancer is 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. DNA microarray technology provides a format for the simultaneous measurement of the expression level of thousands of genes in a single hybridization assay. The most exciting result of microarray technology has been the demonstration that patterns of gene expression can distinguish between tumors of different anatomical origin. Standard statistical methodologies in classification and prediction do not work well or even at all when N (a number of samples) < p (genes). Modification of conventional statistical methodologies or devise of new methodologies is needed for the analysis of colon cancer. Recently, designing artificial neural networks by evolutionary algorithms has emerged as a preferred alternative to the common practice of selecting the apparent best network. In this paper, we propose an evolutionary neural network that classifies gene expression profiles into normal or colon cancer cell. Experimental results on colon microarray data show that the proposed method is superior to other classifiers.
Bibliographical noteFunding Information:
This research was supported by Biometrics Engineering Research Center and Brain Science and Engineering Research Program sponsored by Korean Ministry of Science and Technology.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence