Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction - Chen, Yali; Lei, Xiujuan (Frontiers in Microbiology, 2022)
More and more studies have shown that understanding microbe-disease associations can not only reveal the pathogenesis of diseases, but also promote the diagnosis and prognosis of diseases. Because traditional medical experiments are time-consuming and expensive, many computational methods have been proposed in recent years to identify potential microbe-disease associations. In this article, we propose a method based on heterogeneous network and metapath aggregated graph neural network(MAGNN) to predict microbe-disease associations, called MATHNMDA. First, we introduce microbe-drug interactions, drug-disease associations and microbe-disease associations to construct a microbe-drug-disease heterogeneous network. And we take the heterogeneous network as input to MAGNN. Second, for each layer of MAGNN, we carry out intra-metapath aggregation with multi-head attention mechanism to learn the structural and semantic information embedded in the target node context, the metapath-based neighbor nodes and the context between them, by encoding the metapath instances under the metapath definition mode. We then use inter-metapath aggregation with attention mechanism to combine the semantic information of all different metapaths. Third, we can get the final embedding of microbe nodes and disease nodes based on the output of the last layer in the MAGNN. Finally, we predict potential microbe-disease associations by reconstructing the microbe-disease association matrix. In addition, we evaluated MATHNMDA performance by comparing with its variants, some state-of-the-art methods and different dataset. The results suggest that MATHNMDA is an effective prediction method. The case studies on asthma, inflammatory bowel disease(IBD) and Corona Virus Disease 2019(COVID-19) further validate the effectiveness of MATHNMDA.