FuseLinker: Leveraging LLM's pre-trained text embeddings and domain knowledge to enhance GNN-based link prediction on biomedical knowledge graphs.

Journal: Journal of biomedical informatics
PMID: 39326691

Abstract

OBJECTIVE: To develop the FuseLinker, a novel link prediction framework for biomedical knowledge graphs (BKGs), which fully exploits the graph's structural, textual and domain knowledge information. We evaluated the utility of FuseLinker in the graph-based drug repurposing task through detailed case studies.

Authors

  • Yongkang Xiao
    Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
  • Sinian Zhang
    Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, United States.
  • Huixue Zhou
    Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
  • Mingchen Li
    Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
  • Han Yang
    Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Rui Zhang
    Department of Cardiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.

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