Monte Carlo Simulation Based Gene Selection for Ovarian Cancer Chemotherapy Response Prediction with Microarray Data

The prediction of Chemotherapy response is paramount for personalized ovarian cancer treatment. In this paper, we propose to use Monte Carlo simulation to select gene features for ovarian cancer chemotherapy response prediction with microarray data. Results show that the selected genes not only has comparatively higher classification rate which are independent of classifiers, but also has biological significance. Genes such as FCN3, HSD3B2, BRCA1/2, SLC5A5, ERRS, GPR4 and Rnh1 demonstrate direct relationship with the formation and development of ovarian cancer and are worthy for further biological investigation.