Improved approach for protein function prediction by exploiting prominent proteins

Protein-protein interaction (PPI) networks are valuable biological data source which contain rich information useful for protein function prediction. The PPI network data set obtained from high-throughput experiments is known to be noisy and incomplete. By modeling PPI data as a graph, research efforts are being made in the literature to improve the performance of protein function prediction by extending common neighbor, clustering, and classification based approaches. These approaches exploit the fact that protein shares function with other proteins which are connected through common neighbours. As PPI data is modeled as a graph, it contains prominent nodes which establish relatively high connectivity with other modes. In this paper we propose an improved approach for protein function prediction by exploiting the connectivity properties of prominent proteins. Experimental results on real-world data sets demonstrate the effectiveness of proposed approach.