Heterogeneous graph collaborative representation learning for drug-related microbe prediction with attentive fusion and reciprocal distillation

Abstract

Microbes are microorganisms with biological molecules and have significant therapeutic potential for treating diseases, underscoring the need for computational methods to screen microbes targeting disease-associated drugs. However, the computational methods often consider node embedding or structure features between microbes and drugs, and have a severe class imbalance problem inherent in sparse association data. In this work, we proposed a heterogeneous graph collaborative representation learning model that combines the merits of attentive fusion and reciprocal distillation for drug-related microbe prediction. First, we constructed the heterogeneous biological information and meta-path-induced graphs of microbes and drugs. Then, a topological structure feature encoder is devised to extract complex topological and semantic interaction patterns from heterogeneous biological graphs with microbes and drugs, while an efficient transformer concurrently extracts discriminative semantic and structural information based on the graph position information of nodes. Next, a reciprocal distillation schema is developed to mitigate the adverse effects of the data imbalance problem, and enable the distribution consistency of the model between topological and semantic information extraction. Moreover, we devised a dual collaborative feature fusion schema that combines graph topological and dual meta-path-based semantic features to obtain the discriminative features of microbes and drugs. Through reciprocal distillation, an efficient optimization function focuses on hard-to-classify samples of drug-related microbes via discriminative features. Extensive experiments demonstrate that our model could deal with the association sparsity problem and extract more semantics and structure. Meanwhile, case studies indicate that our model could discover reliable candidate microbes associated with a special drug.