Abstract—Microarray based gene expression profiling has become an important and promising dataset for cancer classification that are used for diagnosis and prognosis purposes. It is important to determine the informative genes that cause the cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. Furthermore, find accurate gene selection method that reduce the dimensionality and select informative genes is very significant issue in cancer classification area. In literature, there are several gene selection methods for cancer classification using microarray dataset. However, most of them did not concern on identifying minimum number of informative genes with high classification accuracy. Therefore, in our research study we discuss the performance of Bio-Inspired evolutionary gene selection method in cancer classification using microarray dataset. And, we prove that the Bio- Inspired evolutionary gene selection methods have superior classification accuracy with minimum number of selected genes.

Index Terms—Bio-inspired evolutionary methods, cancer classification, microarray, gene selection, gene expression.

The authors are with Computer Science Department, King Saud University, Saudi Arabia (tel.: +96612309954; e-mail: halshamlan@ksu.edu.sa, badrghada@hotmail.com, yousef@ksu.edu.sa).

 

Cite: Hala M. Alshamlan, Ghada H. Badr, and Yousef A. Alohali, "The Performance of Bio-Inspired Evolutionary Gene Selection Methods for Cancer Classification Using Microarray Dataset," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 4, no. 3, pp. 166-170, 2014.