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Elife. 2020 Mar 17;9. pii: e52614. doi: 10.7554/eLife.52614.

Wikidata as a knowledge graph for the life sciences.

Author information

1
Micelio, Antwerpen, Belgium.
2
Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, United States.
3
Center for Integrative Bioinformatics Vienna, Max Perutz Laboratories, University of Vienna and Medical University of Vienna, Vienna, Austria.
4
McDonnell Genome Institute, Washington University School of Medicine, St. Louis, United States.
5
Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, United States.
6
European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingdom.
7
School of Chemistry, The University of Sydney, Sydney, Australia.
8
Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, United States.
9
Wellcome Trust Sanger Institute, Cambridge, United Kingdom.
10
School of Data Science, University of Virginia, Charlottesville, United States.
11
University of Maryland School of Medicine, Baltimore, United States.
12
Department of Animal Plant and Soil Sciences, La Trobe University, Melbourne, Australia.
13
Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands.
14
Retired researcher, Berlin, Germany.
15
Yale University Library, Yale University, New Haven, United States.
#
Contributed equally

Abstract

Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.

KEYWORDS:

computational biology; data mining; drug repurposing; knowledge graphs; none; science forum; systems biology; wikidata

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