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.
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