If a gene belongs to more than one mechanism, the greater score was chosen for this gene. We had a total of 827 genes with scores ranging from 1 to 3. In the weight matrix selection step, for each weight matrix, all the 5,055 candidate genes and the core genes were sorted together by their combined scores. Two parameters, Q and g, were introduced to select weight matrices. Matrices that fulfilled these threshold criteria were retained for the next evaluation step. The depression GWA data was utilized to evaluate the performance of each retained weight matrix. For each weight matrix, the p-values distribution of the top j genes and the randomly selected gene set from the GWA data with size j were compared using the Wilcoxon rank-sum test. A significant p-value represents that the p-values distribution in the INCB28060 prioritized set is more significant than in the random set. We generated 1000 random sets in this step for comparisons, and this procedure was repeated 10 times to obtain standard deviation. For every weight matrix, a combined score for each gene could be computed based on the top j ranked prioritized gene set. A cutoff value to choose DEPgenes was determined by a clear separation of combined scores distribution between the core genes and the remaining candidate genes. During these prioritization and evaluation steps, a number of weight matrices passed our selection criteria as candidates for the optimal weight matrix. We applied three approaches to test the robustness of choosing a specific weight matrix as the optimal one to select for DEPgenes. First, we selected ten weight matrices that passed selection criteria to evaluate their performance using the GWA dataset. Second, to investigate whether the rank of prioritized genes obtained from each weight matrix was similar, pair-wise comparisons for the ranks of prioritized genes among ten matrices were calculated using Spearman��s correlation coefficients. A high correlation on average in these comparisons would demonstrate the effectiveness and robustness of this prioritization approach. Third, we investigated the best matrices obtained from our core gene sets with other two alternative core gene sets for the robustness of our DEPgenes selection: core gene sets based on best expression genes and candidate pathway genes. Finally, we evaluated patterns of gene expression of the DEPgenes and nondisease genes in human tissues. Non-disease genes were used as the reference to compare with the DEPgenes. We retrieved human protein-coding genes and 5,139 disease genes from the GeneCards database and obtained a total of 15,874 non-disease genes. We then compared the gene expression patterns between the DEPgenes and non-disease genes in 49 human Niraparib tissues that were extracted from the WebGestalt Tissue Expression using Wilcoxon signed-rank test.