SimpleTrPPI: A simple method for transferring knowledge between interaction networks for PPI prediction

Maps of protein-protein interactions (PPIs) are essential to uncover cellular processes and metabolic processes in a cell. However, various high-throughput biological experiments are time-consuming and labor-intensive, resulting in interactions of high false positive and false negative rates. The fact that most interaction networks remain sparse and incomplete motivates scientists to develop computational methods to predict protein-protein interactions accurately and automatically. However, state-of-the-art prediction algorithms cannot make satisfactory predictions. In this paper, we propose a simple yet effective approach SimpleTrPPI, to improve the accuracy of predicting protein-protein interactions in the target PPI network with the aid of another source PPI network. We attempt to transfer and borrow useful knowledge from the source PPI network using similarities of protein nodes between two protein interaction networks. Similarities are computed taking both protein sequence similarities and topological structures of protein networks into account. Two protein-protein interaction networks, Helicobacter pylori (target network) and Human (source network), are used to verify the feasibility of our proposed method. Our experimental results show that SimpleTrPPI can achieve more than 5% accuracy improvement compared to the baseline methods.