Hypergraph-based logistic matrix factorization for metabolite–disease interaction prediction

Abstract Motivation Function-related metabolites, the terminal products of the cell regulation, show a close association with complex diseases. The identification of disease-related metabolites is critical to the diagnosis, prevention and treatment of diseases. However, most existing computational a...

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Veröffentlicht in:Bioinformatics 2022-01, Vol.38 (2), p.435-443
Hauptverfasser: Ma, Yingjun, Ma, Yuanyuan
Format: Artikel
Sprache:eng
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Zusammenfassung:Abstract Motivation Function-related metabolites, the terminal products of the cell regulation, show a close association with complex diseases. The identification of disease-related metabolites is critical to the diagnosis, prevention and treatment of diseases. However, most existing computational approaches build networks by calculating pairwise relationships, which is inappropriate for mining higher-order relationships. Results In this study, we presented a novel approach with hypergraph-based logistic matrix factorization, HGLMF, to predict the potential interactions between metabolites and disease. First, the molecular structures and gene associations of metabolites and the hierarchical structures and GO functional annotations of diseases were extracted to build various similarity measures of metabolites and diseases. Next, the kernel neighborhood similarity of metabolites (or diseases) was calculated according to the completed interactive network. Second, multiple networks of metabolites and diseases were fused, respectively, and the hypergraph structures of metabolites and diseases were built. Finally, a logistic matrix factorization based on hypergraph was proposed to predict potential metabolite–disease interactions. In computational experiments, HGLMF accurately predicted the metabolite–disease interaction, and performed better than other state-of-the-art methods. Moreover, HGLMF could be used to predict new metabolites (or diseases). As suggested from the case studies, the proposed method could discover novel disease-related metabolites, which has been confirmed in existing studies. Availability and implementation The codes and dataset are available at: https://github.com/Mayingjun20179/HGLMF. Supplementary information Supplementary data are available at Bioinformatics online.
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btab652