Fusion of classifiers for predicting protein-protein interactions

Prediction of protein-protein interaction is a difficult and an important problem in biology. In this paper, we describe a very general method for predicting protein-protein interactions. The interaction mining approach is demonstrated by building a learning system based on experimentally validated protein-protein interactions in the human gastric bacterium Helicobacter pylori. We show that combining linear discriminant classifier and cloud points we obtain an error rate lower than previously published in the literature.