Prediction of Protein-Protein Interactions Based on Attention Diffusion Mechanism in Heterogeneous Information Network

Protein-protein interaction (PPI) prediction is a deep exploration of the mechanism of life activities, but it is costly to rely solely on experimental methods to predict PPI. To accomplish this task, many computational methods have been proposed, but existing methods fail to make full use of the connection between proteins and other molecules, and can not effectively capture the complex semantics between biological entities related to proteins, resulting the poor performance. In this paper, we propose a heterogeneous graph attention diffusion network (HGADN-PPI) to capture the complex semantics in the biological heterogeneous graph for PPI prediction. HGADN-PPI enhances heterogeneous graph learning from the intra-layer perspective and the inter-layer perspective. Concretely, in the intra-layer perspective, we constructed the heterogeneous graph attention diffusion layer (HGADL) which combines biological semantic information from different paths. To enable information passing from important nodes multiple hops away, we use graph diffusion to establish connections between nodes that are not directly connected. In the intra-layer perspective, we enlarge the receptive field by stacking multiple HGADLs. The experimental results demonstrate that the proposed model outperforms the selected representative baselines.