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KG-Hub—building and exchanging biological knowledge graphs

Journal Article · · Bioinformatics
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  1. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  2. University of Colorado, Aurora, CO (United States)
  3. SIB Swiss Institute of Bioinformatics, Basel (Switzerland)
  4. Columbia University Irving Medical Center, New York, NY (United States)
  5. University of Milano (Italy)
  6. MD Anderson Cancer Center, Houston, TX (United States)
  7. Oregon State University, Corvallis, OR (United States)
  8. University of North Carolina, Chapel Hill, NC (United States)
  9. Politecnico di Milano (Italy)
  10. Semanticly, Athens (Greece)
  11. Delphinai Corporation, Sooke, BC (Canada)
  12. The Jackson Laboratory for Genomic Medicine, Farmington, CT (United States)
+ Show Author Affiliations

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract–transform–load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial–environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Basic Energy Sciences (BES); National Institutes of Health (NIH); National Science Foundation (NSF)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
2234091
Journal Information:
Bioinformatics, Journal Name: Bioinformatics Journal Issue: 7 Vol. 39; ISSN 1367-4811
Publisher:
Oxford University PressCopyright Statement
Country of Publication:
United States
Language:
English

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59 BASIC BIOLOGICAL SCIENCES