A Novel Stochastic Block Model for Network-Based Prediction of Protein-Protein Interactions

Abstract

Proteins interact with each other to play critical roles in many biological processes in cells. Although promising, laboratory experiments usually suffer from the disadvantages of being time-consuming and labor-intensive. Hence, a variety of computational approaches have been proposed to predict protein-protein interactions from an alternative view. However, most of them heavily rest on the biological information of proteins while ignoring the latent structural features in protein interaction networks. In this paper, we propose a novel stochastic block model for network-based prediction of protein interactions. By simulating the generative process of a protein interaction network, our approach can capture the latent structural features of proteins from the perspective of forming protein complexes, thus verifying whether two proteins interact with each other or not. To evaluate the performance of the proposed prediction approach, a series of extensive experiments have been conducted and we have also compared our approach with state-of-the-art network-based prediction model. The experiment results show that our approach has a promising performance when applied to predict protein-protein interactions.