An ensemble of K-Local Hyperplane for predicting Protein-Protein interactions

Prediction of protein-protein interaction is a difficult and important problem in biology. In this paper, we propose a new method based on an ensemble of K-Local Hyperplane Distance Nearest Neighbor (HKNN) classifiers, where each HKNN is trained using a different physicochemical property of the amino-acids. Moreover, we propose a new encoding technique that combines the amino acid indices together with the 2-Grams amino-acid composition. A fusion of HKNN classifiers combined with the “Sum rule” enables us to obtain an improvement over the other state-of-the-art methods. The approach is demonstrated by building a learning system based on experimentally validated protein–protein interactions in human gastric bacterium Helicobacter pylori and in Human Dataset.