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Cited by 99Biomedical Text Mining and OntologiesSemantic Web and OntologiesBioinformatics and Genomic Networks

The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information

Open access
Morris, John H, Soman, Karthik, Akbas, Rabia E, Zhou, Xiaoyuan, Smith, Brett, Meng, Elaine C, Huang, Conrad C, Cerono, Gabriel +19 more·2023·Bioinformatics
DOI 10.1093/bioinformatics/btad080PMC PMC9940622
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Abstract

Motivation: Knowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information. Results: In this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a ‘parent table’ of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts. Availability and implementation: The SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

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Crossref · 10.1093/bioinformatics/btad080

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  • The SPOKE KG consists of 42 million nodes of 28 different types and 160 million edges of 91 types, implemented as a property graph and assembled by downloading and integrating information from 41 different biomedical databases.

    “The KG consists of 42 million nodes of 28 different types and 160 million edges of 91 types, implemented as a property graph and is assembled by downloading and integrating information from 41 different biomedical databases.”

    From Biomedical knowledge graph-optimized prompt generation for large language models (2024) · cites this paper as Morris et al. 2023

    The Results section reports more than 27 million nodes of 21 types and 53 million edges of 55 types, contrary to all four size and type counts in the claim. It does confirm integration of 41 biomedical databases and describes a Neo4j graph whose nodes and edges retain properties. (LLM, conf=0.88)

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  • SPOKE's reliability has been demonstrated through its successful application in various biomedical contexts.

    “Thus, while SPOKE’s reliability has been demonstrated through its successful application in various biomedical contexts (Supplementary S1 Text; Himmelstein and Baranzini, 2015, Himmelstein et al. 2017, Nelson et al. 2019, Nelson et al. 2021a,b, Baranzini et al. 2022, Morris et al. 2023, Soman et al. 2023a,b, Tang et al. 2024)”

    From Biomedical knowledge graph-optimized prompt generation for large language models (2024) · cites this paper as Morris et al. 2023

    The Discussion reports that SPOKE has been used in several biomedical applications and that one approach predicted multiple sclerosis diagnoses with up to 83% accuracy. It does not establish SPOKE’s general reliability across those varied contexts; the documented success is specific to that prediction application. (LLM, conf=0.80)

    Experimental — accuracy not yet measured

  • SPOKE integrates more than 40 publicly available biomedical knowledge sources of separate domains where each source is centered around a biomedical concept, such as genes, proteins, drugs, compounds, diseases, and one or more of their known relationships.

    “To achieve this, we make use of the biomedical KG called SPOKE (Morris et al. 2023) that integrates more than 40 publicly available biomedical knowledge sources of separate domains where each source is centered around a biomedical concept, such as genes, proteins, drugs, compounds, diseases, and one or more of their known relationships.”

    From Biomedical knowledge graph-optimized prompt generation for large language models (2024) · cites this paper as Morris et al. 2023

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Cited by

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Authors
Morris, J. H., Soman, K., Akbas, R. E., Zhou, X., Smith, B., Meng, E. C., Huang, C. C., Cerono, G., Schenk, G., Rizk-Jackson, A., Harroud, A., Sanders, L., Costes, S. V., Bharat, K., Chakraborty, A., Pico, A. R., Mardirossian, T., Keiser, M., Tang, A., Hardi, J., Shi, Y., Musen, M., Israni, S., Huang, S., Rose, P. W., Nelson, C. A., & Baranzini, S. E.
Affiliations
  • University of California, San Francisco (US)
  • Institute for Systems Biology (US)
  • Bakar Computational Health Sciences Institute, University of California, San Francisco , San Francisco, CA 94158, USA
  • Ames Research Center (US)
  • Gladstone Institutes (US)
  • Stanford University (US)
  • San Diego Supercomputer Center (US)
Corresponding author
Baranzini, Sergio E
ORCID
Venue
Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2023-02-01
Volume
39
Issue
2
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
DOI
10.1093/bioinformatics/btad080
PMC
PMC9940622
OpenAlex
W4319813194
Cited by
99
References
6
Citations by year
2026: 31 · 2025: 31 · 2024: 25 · 2023: 10 · 2022: 2
FWCI
8.64
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
National Science Foundation (NSF_2033569)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
biomedical knowledge graphs, precision medicine, knowledge graph construction, ontology mapping, biomedical databases, knowledge graph integration, spokes
MeSH terms
Pattern Recognition, Automated, Databases, Factual, Precision Medicine

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