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Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

Fig 6

Prioritizing multiple sclerosis associations identified by a masked GWAS.

From a network with the WTCCC2 MS associations omitted, we predicted probabilities of association for all potentially novel genes. The 37 novel genes identified by the WTCCC2 GWAS were considered positives, and the resulting performance was plotted. The ROC (A) and precision-recall (B) curves show performance, with AUCs in line with the testing performance across all diseases. A prediction threshold (black cross) that resulted in high performance was selected as the discovery threshold for further analysis. As the classification threshold decreases along the precision-recall curve, the advent of each true positive is denoted by its gene symbol.

Fig 6