Exploiting Local and Global Context In PPI networks For Efficient Protein Function Prediction

Protein-protein interaction (PPI) networks are valuable biological data source which contain rich information useful for protein function prediction. The PPI network data obtained from high-throughput experiments is known to be noisy and incomplete. In the literature, common neighbor, clustering, and classification-based approaches have been proposed to improve the performance of protein function prediction by modeling PPI data as a graph. These approaches exploit the fact that protein shares function with other proteins directly interacting with it. In this paper we have experimented an alternative approach by exploiting the notion that two proteins share a function if they have a well defined group of directly or indirectly connected common neighbors. The experiments conducted on variety of PPI network datasets show that the proposed approach improves protein function prediction accuracy over existing approaches.