Biomedical Interaction Prediction with Adaptive Line Graph Contrastive Learning

Abstract

Biomedical interaction prediction is essential for the exploration of relationships between biomedical entities. Predicted biomedical interactions can help researchers with drug discovery, disease treatment, and more. In recent years, graph neural networks have taken advantage of their natural structure to achieve great progress in biomedical interaction prediction. However, most of them use node embedding instead of directly using edge embedding, resulting in information loss. Moreover, they predict links based on node similarity correlation assumptions, which have poor generalization. In addition, they do not consider the difference in topological information between negative and positive sample links, which limits their performance. Therefore, in this paper, we propose an adaptive line graph contrastive (ALGC) method to convert negative and positive sample links into two kinds of line graph nodes. By adjusting the number of intra-class line graph edges and inter-class line graph edges, an augmented line graph is generated and, finally, the information of the two views is balanced by contrastive learning. Through experiments on four public datasets, it is proved that the ALGC model outperforms the state-of-the-art methods.