MGDRGCN: A novel framework for predicting metabolite-disease connections using tripartite network and relational graph convolutional network

Metabolites are fundamental to the existence of biomolecules, and numerous studies have demonstrated that uncovering the connections between metabolites and diseases can enhance our understanding of disease pathogenesis. Traditional biological methods can identify potential metabolite-disease relationships, but these approaches often require significant human and material resources. Consequently, computational methods have emerged as amore efficient alternative. However, most computational methods primarily rely on metabolite- disease associations and rarely explore the impact of more biological entities. To address this issue, we propose a novel computational framework based on a metabolite-gene-disease tripartite heterogeneous network and relational graph convolutional network (R-GCN), abbreviated as MGDRGCN. Specifically, we construct three types of similarity networks from multiple data sources, including metabolite and gene functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity for metabolites and diseases. Next, we use principal component analysis to further extract features and construct a tripartite heterogeneous network with genes as the bridge. This network structure comprehensively captures and represents the complex relationships among metabolites, genes and diseases. We employ R-GCN to extract higher-order information from the tripartite heterogeneous network. Finally, we input the embeddings learned from R-GCN into a residual network classifier to predict potential metabolite-disease associations. In five-fold cross-validation experiments, MGDRGCN exhibit outstanding performance, with both AUC (0.9866) and AUPR (0.9865) significantly surpassing other advanced methods. Additionally, case studies further demonstrate MGDRGCN's superior performance in predicting metabolite-disease associations. Overall, the introduction of MGDRGCN provides new perspectives and methods for future biomedical research, offering promising potential for uncovering the mechanisms of complex biological systems.