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

Abstract

Background:: Microbe-disease associations are integral to understanding complex dis-eases and their screening procedures. Objective:: While numerous computational methods have been developed to detect these associa-tions, their performance remains limited due to inadequate utilization of weighted inherent similari-ties 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 taxono-my common tree to predict microbe-disease associations effectively. The framework extracts mul-tiple microbe similarities from the taxonomy common tree, facilitating the construction of a mi-crobe-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 su-periority 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.