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It can help to find significantly affected pathways within a gene expression data. The IPAD contains about 22,498 genes, selleck chemicals llc 25,469 proteins, 1,956 pathways, 6,704 diseases, 5,615 drugs, and 52 organs integrated from databases including the BioCarta,17 KEGG,21 NCI-Nature curated,22 Reactome,23 CTD,24 PharmGKB,25 DrugBank,26 and HOMER.27 It can provide reliable pathway�Cgene relationship for the gene set database in GSEA. The 16 pathway-based biomarkers we identified are signaling, complement pathway, binding receptors, and metabolism (Table 1), which are consistent with previous findings.28 We further evaluated the prediction performance of our pathway-based biomarkers by comparing our results with prediction performances in previously published findings. For example, Aaroe et al identified a set of 738 differentially expressed probes that achieved an estimated prediction accuracy of 79.5% with a sensitivity of 80.6% and a specificity of 78.3%28 and Sharma et al identified a panel of 37 genes that permitted early detection with a classification accuracy of 82%.29 These prediction results were based on the training set, not the independent testing set. For the testing set, Histone demethylase our pathway-based biomarkers show a similar prediction performance to theirs. However, for the training set, prediction performance of our pathway-based biomarkers is higher than theirs (Table 2). In addition, we also did the feature selection based on all proteins. With a P value cutoff Enzalutamide purchase = 90.00%, sensitivity = 92.50%, specificity = 87.50%) and for the testing set (AUC = 0.9188, precision = 81.25%, accuracy = 87.50%, sensitivity = 97.50%, specificity = 77.50%). The prediction performances based on proteins are lower than the highest performance of our pathway-based approach. When we chose the top 17 proteins in Study A as biomarkers, we obtained lower prediction performance (AUC = 0.8138 for the testing set) than the mean performance based on the pathway-based biomarkers (AUC = 0.8350 for the testing set). An interesting observation in our study is that some of genes in the pathway-based biomarker are not differentially expressed between cancer and normal, for example, in the GPCR downstream signaling pathway, ADRBK1 (P value = 0.68), AGT (P value = 0.94) and OR7D4 (P value = 0.69) with high a P value. After we removed all proteins with P value �� 0.001 in the pathway, the prediction performances dropped dramatically (Table 4). It suggests that the genes with a high P value can still be valuable in a pathway, compared with conventional methods, which usually limit genes to those with change below a P value threshold such as 0.001.