Authors : S. Subasree, N.P. Gopalan and N.K. Sakthivel
Abstract: Microarray based Cancer Pattern Classification and Prediction technique is one of the most efficient mechanisms in Bioinformatics research. This research work studied and analyzed thousands of genes simultaneously to understand the pattern of the gene expression. This research work focuses to identify and prioritize genes that are important for gene patterns classification and prediction. This research work proposed an Enhanced Cancer-Association based Gene Selection technique for Cancer Patterns Classification and Prediction (ECAGS). The proposed classifier is implemented and studied thoroughly in terms of memory utilization, execution time (processing time), classification accuracy, sensitivity, specificity and F score. The experimental results were compared with our previous model called an Enhanced Multi-Objective Particle Swarm (EMOPS). From our experimental results, it was noticed that the proposed model outperforms our previous model in terms of memory utilization, execution time (processing time), classification accuracy, sensitivity, specificity and F score.
S. Subasree, N.P. Gopalan and N.K. Sakthivel, 2019. ECAGS: An Enhanced Cancer-Association based Gene Selection Technique for Cancer Patterns Classification and Prediction. Journal of Engineering and Applied Sciences, 14: 8080-8087.