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This study created a comprehensive coronavirus knowledge graph by integrating drug discovery data and scientific articles. The knowledge graph aids in understanding COVID-19 by revealing relationships between genes, diseases, and potential treatments.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Data Science

Background:

  • The COVID-19 pandemic necessitates integrated knowledge for drug discovery and research.
  • Existing datasets are fragmented, hindering comprehensive analysis of the viral domain.

Purpose of the Study:

  • To construct a unified coronavirus knowledge graph (KG) by merging diverse data sources.
  • To enhance the understanding of COVID-19 related biological entities and their relationships.

Main Methods:

  • Integrated the Analytical Graph (AG) drug discovery dataset with the CORD-19 article collection.
  • Performed entity disambiguation on CORD-19 data using Wikidata.
  • Populated the KG with over 21,700 genes, 2,500 diseases, and 94,000 phenotypes, defining 27 relationship types.

Main Results:

  • The resulting KG contains a rich network of chemo genomic and biological entities relevant to COVID-19.
  • Analysis of an ego-centered network around angiotensin-converting enzyme (ACE) provided COVID-19 insights.
  • Identified significant COVID-19-related paths between entities like hydroxychloroquine and IL-6 receptor.

Conclusions:

  • The developed KG offers a valuable resource for COVID-19 research and drug discovery.
  • The KG's structure facilitates the exploration of complex biological relationships and potential therapeutic strategies.