Abstract

Protein-protein interactions play an important role in biological and cellular processes. Biochemistry experiment is the most reliable approach identifying protein-protein interactions, but it is time-consuming and expensive. It is one of the important reasons why there is only a little fraction of complete protein-protein interactions networks available by far. Hence, accurate computational methods are in a great need to predict protein-protein interactions. In this work, we proposed a new weighted feature fusion algorithm for protein-protein interactions prediction, which extracts both protein sequence feature and evolutionary feature, for the purpose to use both global and local information to identify protein-protein interactions. The method employs maximum margin criterion for feature selection and support vector machine for classification. Experimental results on 11188 protein pairs showed that our method had better performance and robustness. Performed on the independent database of Helicobacter pylori, the method achieved 99.59% sensitivity and 93.66% prediction accuracy, while the maximum margin criterion is 88.03%. The results indicated that our method was more efficient in predicting protein-protein interaction compared with other six state-of-the-art peer methods.

Keywords: PPI, PPIs prediction, MMC, feature selection, SVM, DNA.