A novel ensemble of probabilistic neural network for predicting protein-protein interactions

The knowledge of protein-protein interactions (PPIs) in cells is indispensable for deep understanding the biological process. Although many computational methods have been developed for identification of PPIs, there are still many difficulties due to high computation complexity and noisy data. In this paper, we proposed an ensemble of probabilistic neural network (PNN) to predict PPIs from primary sequence which achieved promising results. The key advantage of the algorithm is that it combines variety of physicochemical property features to construct diverse individual classifiers for ensemble prediction. What makes the method much more attractive is that it not only generated much more diverse and robust individual classifiers, but also contains different interaction physicochemical information which dictated the structure and the function of proteins. Moreover, the PNN is robust to noise and trained easily, it is suitable for dealing with the large scale noisy PPIs data. Experiment results on H. pylori and Human datasets show that our proposed method performs at least 8% higher accuracy than the best of other related works.