Predicting Microbe-Disease Association Based on Enhanced Relational Graph Convolutional Networks

Microbial-disease association prediction has always been a frontier research direction in bioinformatics, which includes two tasks: predicting association relationships and association categories (Decrease, Increase). At present, The study methods based on statistical analysis rely heavily on biological prior knowledge, the traditional microbial experiments are time-consuming and costly. In this paper, we propose a novel model that predict microbe-disease association based on an enhanced relational graph convolutional network. Firstly, we construct a heterogeneous network containing microbial abundance changes and disease associations, microbial similarity, and disease similarity. Then, this paper proposes a feature difference enhancement module based on graph similarity, which aims to enhance the difference of feature representation between different association categories. It is fused with the relational graph convolutional network to form an enhanced relational graph convolutional network and complete feature coding work. Thus, the multi-category feature representation between nodes can be effectively extracted and the influence of edges on the graph structure can be considered. Finally, a neural network was used to complement nonlinear feature representation. The experimental results show that the proposed model can efficiently predict the association categories between microbial abundance changes and diseases.