BBLN: A bilateral-branch learning network for unknown protein–protein interaction prediction

Abstract

Unknown Protein–Protein Interactions (PPIs) prediction has a huge demand in the biological analysis field. Since the effect of the limited availability of protein data is severe, transferable representations are highly demanded to be learned from various data. The latest works enhance the model performance on unknown PPIs prediction and have achieved certain improvements by combining protein information and relation information on PPI graph. However, such methods inevitably suffer from a so-called information monotonicity problem that limits the improvements when encountering large amounts of unknown PPIs. The prediction performance cannot be actually increased without considering the complementary information and relationship information among various modalities of protein data. To this end, we propose a bilateral-branch learning network to deeply enhance the both complementary and relationship information based on the amino acid sequence and gene ontology from multi- and cross-modal views. Experimental results on massive real-world datasets show that our method significantly outperforms the previous state-of-the-art on both traditional and novel unknown PPIs prediction.