Prediction of Protein Functions from Protein-Protein Interaction Data Based on a New Measure of Network Betweenness

Assigning functions to proteins that have not been annotated is an important problem in the post-genomic era. Meanwhile, the availability of data on protein-protein interactions provides a new way to predict protein functions. Previously, several computational methods have been developed to solve this problem. Among them, Deng et al. developed a method based on the Markov random field (MRF). Lee et al. extended it to the kernel logistic regression model (KLR) based on the diffusion kernel. These two methods were tested on yeast benchmark data, and the results demonstrated that both MRF and KLR had high precision in function prediction. On that basis, inspired by the idea of a Markov cluster algorithm, we defined a new measure of network betweenness, and developed a betweenness-based logistic regression model (BLR). Applying it to predict protein functions on the yeast benchmark data, we found that BLR outperformed both the KLR and the MRF models. It is evidently that BLR is a more proper and efficient approach of function prediction.