A Multimodal Joint Representation Framework for Microbe–drug Association Prediction

The biomedical literature suggests that drug interventions can affect microbial communities of the human body and thereby treat disease. Screening of microbe-drug associations is therefore a key research topic in the field of antimicrobial drug development and drug repositioning. Existing models have not adequately integrated the multimodal representations of biomedical entities and the microbe-drug association networks from different sources. In addition, the positive negative sample imbalance of the heterogeneous networks and the sparsity of the association matrices decrease the accuracy of the prediction results. In this paper, we proposed a multimodal joint representation framework (MJRFMDA) for microbe-drug association prediction. First, we represented microbial and drug features through multimodal attributes. Secondly, we aggregated neighbor information and updated node attributes through GCN and GAT in microbe-drug association networks. Then we aggregated the embedded representations of multimodal networks through a joint representation layer based on a graph-level attention mechanism. Finally, we employed neural inductive matrix completion to capture the nonlinear associations between microbes and drugs, and reconstructed the association score matrix. The experimental results show that MJRFMDA outperforms the six selected state-of-the-art models.