Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature.

Journal: Journal of biomedical informatics
Published Date: Jul 1, 2022

Abstract

OBJECTIVE: Develop a novel methodology to create a comprehensive knowledge graph (SuppKG) to represent a domain with limited coverage in the Unified Medical Language System (UMLS), specifically dietary supplement (DS) information for discovering drug-supplement interactions (DSI), by leveraging biomedical natural language processing (NLP) technologies and a DS domain terminology.

Authors

  • Dalton Schutte
    Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; Department of Pharmaceutical Care & Health Systems, University of Minnesota, Minneapolis, MN, USA.
  • Jake Vasilakes
    Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
  • Anu Bompelli
    Department of Pharmaceutical Care & Health Systems, University of Minnesota, Minneapolis, MN, USA.
  • Yuqi Zhou
    Sun Yat-sen University, The Third Affiliated Hospital, Guangzhou, 510640, China. zhouyuqi@mail.sysu.edu.cn.
  • Marcelo Fiszman
    Lister Hill National Center for Biomedical Communications U.S. National Library of Medicine Bethesda, MD.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Halil Kilicoglu
    School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL 61820, United States.
  • Jeffrey R Bishop
    Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, United States.
  • Terrence Adam
    Institute for Health Informatics.
  • Rui Zhang
    Department of Cardiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.

Keywords

External Resources

Popular Topics
Recent Journals