Protocol to implement a computational pipeline for biomedical discovery based on a biomedical knowledge graph.

Journal: STAR protocols
Published Date: Oct 25, 2023

Abstract

Biomedical knowledge graphs (BKGs) provide a new paradigm for managing abundant biomedical knowledge efficiently. Today's artificial intelligence techniques enable mining BKGs to discover new knowledge. Here, we present a protocol for implementing a computational pipeline for biomedical knowledge discovery (BKD) based on a BKG. We describe steps of the pipeline including data processing, implementing BKD based on knowledge graph embeddings, and prediction result interpretation. We detail how our pipeline can be used for drug repurposing hypothesis generation for Parkinson's disease. For complete details on the use and execution of this protocol, please refer to Su et al..

Authors

  • Chang Su
    Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut.
  • Yu Hou
    Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA.
  • Michael Levin
    Department of Biology, Allen Discovery Center at Tufts University, Tufts University, 200 Boston Ave. Suite 4604, Medford, MA, 02155, USA.
  • Rui Zhang
    Department of Cardiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
  • Fei Wang
    Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.

Keywords

External Resources

Popular Topics
Recent Journals