Title :
Monte Carlo Simulation Based Gene Selection for Ovarian Cancer Chemotherapy Response Prediction with Microarray Data
Author :
Xie, Ruifei ; Han, Bin ; Li, Lihua ; Wang, Qing ; Zhu, Lei ; Dai, Qi
Author_Institution :
Coll. of Life Inf. Sci. & Instrum. Eng., Hangzhou Dianzi Univ., Hangzhou, China
Abstract :
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.
Keywords :
Monte Carlo methods; biological organs; cancer; data analysis; genetics; genomics; gynaecology; medical computing; patient treatment; BRCA1/2 genes; ERRS genes; FCN3 genes; GPR4 genes; HSD3B2 genes; Monte Carlo simulation; Rnh1 genes; SLC5A5 genes; data classification; gene selection; microarray data; ovarian cancer chemotherapy; Bioinformatics; Cancer; Immune system; Monte Carlo methods; Proteins; Tumors;
Conference_Titel :
Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5088-6
DOI :
10.1109/icbbe.2011.5780041