Augmented transitive relationships with high impact protein distillation in protein interaction prediction

Predicting new protein–protein interactions is important for discovering novel functions of various biological pathways. Predicting these interactions is a crucial and challenging task. Moreover, discovering new protein–protein interactions through biological experiments is still difficult. Therefore, it is increasingly important to discover new protein interactions. Many studies have predicted protein–protein interactions, using biological features such as Gene Ontology (GO) functional annotations and structural domains of two proteins. In this paper, we propose an augmented transitive relationships predictor (ATRP), a new method of predicting potential protein interactions using transitive relationships and annotations of protein interactions. In addition, a distillation of virtual direct protein–protein interactions is proposed to deal with unbalanced distribution of different types of interactions in the existing protein–protein interaction databases. Our results demonstrate that ATRP can effectively predict protein–protein interactions. ATRP achieves an 81% precision, a 74% recall and a 77% F-measure in average rate in the prediction of direct protein–protein interactions. Using the generated benchmark datasets from KUPS to evaluate of all types of the protein–protein interaction, ATRP achieved a 93% precision, a 49% recall and a 64% F-measure in average rate. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.

► We use a model of augmented transitive relationship to predict protein interaction.
► We propose a distillation method to deal with incomplete interaction data.
► Augmented Transitive Relationship Predictor achieves a good prediction rate.
► The proposed distillation method will improve the recall rate.