Fig. 1
From: Differential connectivity of splicing activators and repressors to the human spliceosome
Workflow of the Bayesian probability model to predict protein-protein interactions. Example of how the probability of direct interaction (Pin) between SRSF1 and TRA2B was calculated. a We first extracted all known PPIs formed by SRSF1 or TRA2B from a PPI database. b We used the number of shared PPIs between both proteins (blue nodes) and exclusive PPIs (white nodes) to calculate the Transitivity (T). c We then extracted their co-expression profile from the BioGPS microarray database and computed the Pearson correlation coefficient (C). d By transforming the calculated values of T and C through conditional-probability models, we estimated the probability that both T and C may occur in a true PPI network (e = 1, left network) and a false (that is, shuffled) interactome (e = 0, right network). e Finally, the probability Pin was calculated using the Bayes rule, as the posterior probability that SRSF1 and TRA2B directly bind each other, given T and C as evidence