Enhancing Disease-Metabolite Associations Prediction With Weighted Graph Convolutional Networks Based on Community-Driven Link Completion

Metabolites, the end products of biological processes, hold immense potential as biomarkers for elucidating disease mechanisms. Given the costs associated with wet-lab experiments, computational methods offer a viable solution to narrow the search for candidate metabolites. However, sparse disease-metabolite associations pose a challenge to current deep learning-based methods, limiting comprehensive feature learning. To address this, we propose a novel Weighted Graph Convolutional Network approach based on Community-Driven Link Completion (WGCNCDLC) to improve the predictive accuracy of disease-metabolite associations. Specifically, we construct disease and metabolite similarity networks to complete potential links between same-type nodes. Furthermore, we partition the similarity networks into multiple communities and calculate the connection strengths between disease and metabolite communities to enrich the sparse links between different-type nodes. The completed network enables richer feature capture in subsequent weighted graph convolutional network encoding. Experimental results on two datasets show our WGCNCDLC model outperforms nine state-of-the-art algorithms. Case studies on Alzheimer's disease and asthma further validate the model's reliability as a predictive tool for potential metabolite discovery.