Identification of metabolite-disease associations based on knowledge graph.

Journal: Metabolomics : Official journal of the Metabolomic Society
PMID: 39987424

Abstract

BACKGROUND: Despite the insights that metabolite analysis can provide into the onset, development, and progression of diseases-thus offering new concepts and methodologies for prevention, diagnosis, and treatment-traditional wet lab experiments are often time-consuming and labor-intensive. Consequently, this study aimed to develop a machine learning model named COM-RAN, which is based on a knowledge graph and random forest algorithm, to identify potential associations between metabolites and diseases.

Authors

  • Fuheng Xiao
    School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, P.R. China.
  • Canling Huang
    School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, P.R. China.
  • Ali Chen
    Center for Drug Research and Development, Guangdong Provincial Key Laboratory of Advanced Drug Delivery System, Guangdong Pharmaceutical University, Guangzhou, 510006, P.R. China.
  • Wei Xiao
    Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhenjiang Province, China.
  • Zhanchao Li
    School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, People's Republic of China.

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