Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science. Issue 8 (6th June 2022)
- Record Type:
- Journal Article
- Title:
- Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science. Issue 8 (6th June 2022)
- Main Title:
- Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science
- Authors:
- Unni, Deepak R.
Moxon, Sierra A. T.
Bada, Michael
Brush, Matthew
Bruskiewich, Richard
Caufield, J. Harry
Clemons, Paul A.
Dancik, Vlado
Dumontier, Michel
Fecho, Karamarie
Glusman, Gustavo
Hadlock, Jennifer J.
Harris, Nomi L.
Joshi, Arpita
Putman, Tim
Qin, Guangrong
Ramsey, Stephen A.
Shefchek, Kent A.
Solbrig, Harold
Soman, Karthik
Thessen, Anne E.
Haendel, Melissa A.
Bizon, Chris
Mungall, Christopher J. - Other Names:
- Acevedo Liliana investigator.
Ahalt Stanley C. investigator.
Alden John investigator.
Alkanaq Ahmed investigator.
Amin Nada investigator.
Avila Ricardo investigator.
Balhoff Jim investigator.
Baranzini Sergio E. investigator.
Baumgartner Andrew investigator.
Baumgartner William investigator.
Belhu Basazin investigator.
Brandes MacKenzie investigator.
Brandon Namdi investigator.
Burtt Noel investigator.
Byrd William investigator.
Callaghan Jackson investigator.
Cano Marco Alvarado investigator.
Carrell Steven investigator.
Celebi Remzi investigator.
Champion James investigator.
Chen Zhehuan investigator.
Chen Mei‐Jan investigator.
Chung Lawrence investigator.
Cohen Kevin investigator.
Conlin Tom investigator.
Corkill Dan investigator.
Costanzo Maria investigator.
Cox Steven investigator.
Crouse Andrew investigator.
Crowder Camerron investigator.
Crumbley Mary E. investigator.
Dai Cheng investigator.
Dančík Vlado investigator.
De Miranda Azevedo Ricardo investigator.
Deutsch Eric investigator.
Dougherty Jennifer investigator.
Duby Marc P. investigator.
Duvvuri Venkata investigator.
Edwards Stephen investigator.
Emonet Vincent investigator.
Fehrmann Nathaniel investigator.
Flannick Jason investigator.
Foksinska Aleksandra M. investigator.
Gardner Vicki investigator.
Gatica Edgar investigator.
Glen Amy investigator.
Goel Prateek investigator.
Gormley Joseph investigator.
Greyber Alon investigator.
Haaland Perry investigator.
Hanspers Kristina investigator.
He Kaiwen investigator.
He Kaiwen investigator.
Henrickson Jeff investigator.
Hinderer Eugene W. investigator.
Hoatlin Maureen investigator.
Hoffman Andrew investigator.
Huang Sui investigator.
Huang Conrad investigator.
Hubal Robert investigator.
Huellas‐Bruskiewicz Kenneth investigator.
Huls Forest B. investigator.
Hunter Lawrence investigator.
Hyde Greg investigator.
Issabekova Tursynay investigator.
Jarrell Matthew investigator.
Jenkins Lindsay investigator.
Johs Adam investigator.
Kang Jimin investigator.
Kanwar Richa investigator.
Kebede Yaphet investigator.
Kim Keum Joo investigator.
Kluge Alexandria investigator.
Knowles Michael investigator.
Koesterer Ryan investigator.
Korn Daniel investigator.
Koslicki David investigator.
Krishnamurthy Ashok investigator.
Kvarfordt Lindsey investigator.
Lee Jay investigator.
Leigh Margaret investigator.
Lin Jason investigator.
Liu Zheng investigator.
Liu Shaopeng investigator.
Ma Chunyu investigator.
Magis Andrew investigator.
Mamidi Tarun investigator.
Mandal Meisha investigator.
Mantilla Michelle investigator.
Massung Jeffrey investigator.
Mauldin Denise investigator.
McClelland Jason investigator.
McMurry Julie investigator.
Mease Philip investigator.
Mendoza Luis investigator.
Mersmann Marian investigator.
Mesbah Abrar investigator.
Might Matthew investigator.
Morton Kenny investigator.
Muller Sandrine investigator.
Muluka Arun Teja investigator.
Osborne John investigator.
Owen Phil investigator.
Patton Michael investigator.
Peden David B. investigator.
Peene R. Carter investigator.
Persaud Bria investigator.
Pfaff Emily investigator.
Pico Alexander investigator.
Pollard Elizabeth investigator.
Price Guthrie investigator.
Raj Shruti investigator.
Reilly Jason investigator.
Riutta Anders investigator.
Roach Jared investigator.
Roper Ryan T. investigator.
Rosenblatt Greg investigator.
Rubin Irit investigator.
Rucka Sienna investigator.
Rudavsky‐Brody Nathaniel investigator.
Sakaguchi Rayn investigator.
Santos Eugene investigator.
Schaper Kevin investigator.
Schmitt Charles P. investigator.
Schurman Shepherd investigator.
Scott Erik investigator.
Seitanakis Sarah investigator.
Sharma Priya investigator.
Shmulevich Ilya investigator.
Shrestha Manil investigator.
Shrivastava Shalki investigator.
Sinha Meghamala investigator.
Smith Brett investigator.
Southall Noel investigator.
Southern Nicholas investigator.
Stillwell Lisa investigator.
Strasser Michael " Michi" investigator.
Su Andrew I. investigator.
Ta Casey investigator.
Thessen Anne E. investigator.
Tinglin Jillian investigator.
Tonstad Lucas investigator.
Tran‐Nguyen Thi investigator.
Tropsha Alexander investigator.
Vaidya Gaurav investigator.
Veenhuis Luke investigator.
Viola Adam investigator.
von Grotthuss Marcin investigator.
Wang Max investigator.
Wang Patrick investigator.
Watkins Paul B. investigator.
Weber Rosina investigator.
Wei Qi investigator.
Weng Chunhua investigator.
Whitlock Jordan investigator.
Williams Mark D. investigator.
Williams Andrew investigator.
Womack Finn investigator.
Wood Erica investigator.
Wu Chunlei investigator.
Xin Jiwen Kevin investigator.
Xu Hao investigator.
Xu Colleen investigator.
Yakaboski Chase investigator.
Yao Yao investigator.
Yi Hong investigator.
Yilmaz Arif investigator.
Zheng Marissa investigator.
Zhou Xinghua investigator.
Zhou Eric investigator.
Zhu Qian investigator.
Zisk Tom investigator.
… (more) - Abstract:
- Abstract: Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with otherAbstract: Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science. … (more)
- Is Part Of:
- Clinical and translational science. Volume 15:Issue 8(2022)
- Journal:
- Clinical and translational science
- Issue:
- Volume 15:Issue 8(2022)
- Issue Display:
- Volume 15, Issue 8 (2022)
- Year:
- 2022
- Volume:
- 15
- Issue:
- 8
- Issue Sort Value:
- 2022-0015-0008-0000
- Page Start:
- 1848
- Page End:
- 1855
- Publication Date:
- 2022-06-06
- Subjects:
- Medicine, Experimental -- Periodicals
Medical innovations -- Periodicals
616.027 - Journal URLs:
- http://www3.interscience.wiley.com/journal/118902557/home ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1111/cts.13302 ↗
- Languages:
- English
- ISSNs:
- 1752-8054
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3286.255400
British Library DSC - BLDSS-3PM
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- 23061.xml