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

Fig 5

Decomposing performance shows the superiority of the integrative model and compares individual features.

Disease, feature, and model-specific performance on the complete network. The AUROC (y-axis) was calculated for each classifier (x-axis). In addition to the ridge and lasso models (rightmost panels), each feature was considered as a classifier. Line segments show the classifier’s global performance (average performance across permuted networks shown in violet as opposed to dark grey). Points indicate disease-specific performance and are colored by the disease’s pathophysiology. Grey rectangles show the 95% confidence interval for mean disease-specific performance. A) Features from metapaths that traverse an MSigDB collection. B) Features from non-MSigDB-traversing metapaths. Metapaths are abbreviated using first letters of metanodes (uppercase, Table 1) and metaedges (lowercase, Table 2). Feature descriptions are provided in S1 Table.

Fig 5