Hyperplanes for predicting protein-protein interactions

Prediction of protein-protein interaction is a difficult and important problem in biology. Given (numerical) features, one of the existing machine learning techniques can be then applied to learn and classify proteins represented by these features. Our computational results demonstrate that a system based on K-local hyperplane outperforms the methods proposed in the literature based on global representation of a protein pair. The approach is demonstrated by building a learning system based on experimentally validated protein-protein interactions in the human gastric bacterium Helicobacter pylori dataset and in Human dataset.