Using Topology Information for Protein-Protein Interaction Prediction

The reconstruction of protein-protein interaction networks is nowadays an important challenge in systems biology. Computational approaches can address this problem by complementing high-throughput technologies and by helping and guiding biologists in designing new laboratory experiments. The proteins and the interactions between them form a network, which has been shown to possess several topological properties. In addition to information about proteins and interactions between them, knowledge about the topological properties of these networks can be used to learn accurate models for predicting unknown protein-protein interactions. This paper presents a principled way, based on Bayesian inference, for combining network topology information jointly with information about proteins and interactions between them. The goal of this combination is to build accurate models for predicting protein-protein interactions. We define a random graph model for generating networks with topology similar to the ones observed in protein-protein interaction networks. We define a probability model for protein features given the absence/presence of an interaction and combine this with the random graph model by using Bayes' rule, to finally arrive at a model incorporating both topological and feature information.