A FLEXIBLE NONPARAMETRIC APPROACH TO FIND CANDIDATE GENES ASSOCIATED WITH DISEASE IN MICROARRAY EXPERIMENTS AHMED HOSSAIN * ,§, ** , ANDREW R. WILLAN * ,†,¶ and JOSEPH BEYENE ‡,||, ** * Dalla Lana School of Public Health University of Toronto, 155 College Street Toronto, ON M5T 3M7, Canada † Program in Child Health Evaluative Sciences SickKids Research Institute, 555 University Avenue Toronto, ON M5G 1X8, Canada ‡ Department of Clinical Epidemiology & Biostatistics McMaster University, Hamilton Ontario L8S 4L8, Canada § ahmed.hossain@utoronto.ca ¶ andy@andywillan.com || beyene@mcmaster.ca Received 17 September 2011 Revised 12 April 2012 Accepted 13 August 2012 Published 23 October 2012 Very often biologists are interested to know the biological function of a particular gene. Its true biological function may depend on other genes. Finding other genes in the same biological pathway of that gene may enhance further understanding of its biological function. Therefore, we are interested in ¯nding other candidate genes whose expression values are highly correlated with that of a \seed" gene. The \seed" gene, which is known and associated with a disease, is used as a reference to extract candidate genes from microarray experiments and enriched pathways. We propose a nonparametric procedure for selecting the candidate genes. The ex- pression levels for these candidate genes are correlated with that of a \seed" gene in microarray experiments. The proposed test statistic compares two Area Under Receiver Operating Char- acteristic Curves (AUC) for gene pairs, taking implicit correlation between two AUCs into account. The performance of our method is compared to the other well-known methods through the use of simulation and real data analysis. Keywords: Microarray experiments; receiver operating characteristic curve; pathways; false discovery rate. ** Corresponding authors. Journal of Bioinformatics and Computational Biology Vol. 11, No. 2 (2013) 1250021 (19 pages) # . c Imperial College Press DOI: 10.1142/S0219720012500217 1250021-1