Prediction of protein function using common-neighbors in protein-protein interaction networks

The recent high-throughput bio-techniques have provided us large-scale protein-protein interaction data through systematic identification of physical and genetic interactions among all proteins in an organism. Several previous studies have shown that using protein-protein interaction networks to predict protein function is a big step toward full understanding of the mechanisms of cells. However, the protein-protein interaction data derived from high-throughput experiments are typically very noisy, which presents great challenges to the existing methods. In this paper, we propose a novel common-neighborbased model and a Bayesian framework to predict protein function on the basis of the small-world property of the protein-protein interaction network. We tested our approach on five data sets from various sources. The experimental results have shown that our approach has a better performance than several representative methods in terms of both precision and recall. In addition, our method is particularly effective to handle the high false-positive and false-negative rates in protein-protein interaction data.