Improving domain-based protein interaction prediction using biologically-significant negative dataset

We propose a domain-based classification method to predict protein-protein interactions using probabilities of putative interacting domain pairs derived from both experimentally-determined interacting protein pairs and carefully-chosen non-interacting protein pairs. Multi-species comparative results for protein interaction prediction show that such careful generation of biologically meaningful negative training data can improve classification performance.