Abstract: Gene selection is very important in classification of cancer using parallel computing in the analysis of gene expression relationship. The high performance parallel computing is used for gene expression analysis and finding the thousands of genes simultaneously. DNA microarrays are used to measure the expression levels of thousands of genes simultaneously. The classification and validation of molecular biomarkers for cancer diagnosis is an important problem in cancer genomics. The microarray data analysis is very much important to extract biologically useful data from the huge amount of expression data to know the current state of the cell. Most cellular processes are regulated by changes in gene expression. This is a great challenge for computational biologists who see in this new technology the opportunity to discover interactions between genes. In this study, we propose a Cooperative Parallel Multi-Objective Genetic algorithm for Gene Feature Selection. We have implemented CPMGA for gene feature selection to classify the breast cancer data sets. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers.
A. Natarajan and T. Ravi, 2014. Cooperative Parallel Multi-Objective Genetic Algorithm for Gene Feature Selection to Diagnose Breast Cancer. Asian Journal of Information Technology, 13: 761-769.