Assessing and predicting protein interactions using both local and global network topological metrics.

High-throughput protein interaction data, with ever-increasing volume, are becoming the foundation of many biological discoveries. However, high-throughput protein interaction data are often associated with high false positive and false negative rates. It is desirable to develop scalable methods to identify these errors. In this paper, we develop a computational method to identify spurious interactions and missing interactions from high-throughput protein interaction data. Our method uses both local and global topological information of protein pairs, and it assigns a local interacting score and a global interacting score to every protein pair. The local interacting score is calculated based on the common neighbors of the protein pairs. The global interacting score is computed using globally interacting protein group pairs. The two scores are then combined to obtain a final score called LGTweight to indicate the interacting possibility of two proteins. We tested our method on the DIP yeast interaction dataset. The experimental results show that the interactions ranked top by our method have higher functional homogeneity and localization coherence than existing methods, and our method also achieves higher sensitivity and precision under 5-fold cross validation than existing methods.