Enhancing Disease-Metabolite Associations Prediction With Weighted Graph Convolutional Networks Based on...

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.