Figure 3.

MAPK interactome analysis and validation. (a) Overlap of the Vinayagam (blue) and Bandyopadhyay (red) datasets (left). The study by Bandyopadhyay et al. reveals 2,269 interactions with 641 'core' interactions supported by multiple lines of evidence, whereas the Vinayagam dataset has 2,626 interactions connecting 1,126 proteins. Differences in the two experimental techniques are highlighted by the fact that only 170 nodes and 6 interactions overlap in the two sets. (b) Coev2Net predicted high-confidence network is shown on the right. Edge colors correspond to the dataset they come from. MAPK6 has the highest degree, and its label is shown explicitly. (c) Comparisons of performance on MAPK network for Coev2Net and Struct2Net (iWRAP+DBLRAP) [32,49,66] in terms of sensitivity and specificity. Coev2Net performs much better than previous methods on this dataset (core network of Bandyopadhyay et al.), and its performance is robust with respect to the randomness in MCMC sampling. The classifier (Figure 2) is trained and tested via five-fold cross-validation on the core network. The MCMC procedure is repeated five times to assess robustness of the predictions and the corresponding error bars are indicated. 'Baseline' method represents a logistic regression classifier with just the alignment features and no PPI (inter-protein) features. (d) Experimental validation of predicted high-confidence interactions using LUMIER assay. Typically a fold increase of 1.5 is considered as a true positive.