Discovering Microbe-disease Associations with Weighted Graph Convolution Networks and Taxonomy Common Tree

Background: Microbe-disease associations are integral to understanding complex diseases and their screening procedures. Objective: While numerous computational methods have been developed to detect these associations, their performance remains limited due to inadequate utilization of weighted inherent similarities and microbial taxonomy hierarchy. To address this limitation, we have introduced WTHMDA (weighted taxonomic heterogeneous network-based microbe-disease association), a novel deep learning framework. Methods: WTHMDA combines a weighted graph convolution network and the microbial taxonomy common tree to predict microbe-disease associations effectively. The framework extracts multiple microbe similarities from the taxonomy common tree, facilitating the construction of a microbe- disease heterogeneous interaction network. Utilizing a weighted DeepWalk algorithm, node embeddings in the network incorporate weight information from the similarities. Subsequently, a deep neural network (DNN) model accurately predicts microbe-disease associations based on this interaction network. Results: Extensive experiments on multiple datasets and case studies demonstrate WTHMDA's superiority over existing approaches, particularly in predicting unknown associations. Conclusion: Our proposed method offers a new strategy for discovering microbe-disease linkages, showcasing remarkable performance and enhancing the feasibility of identifying disease risk.