The COVID-19 Ontology
Abstract Motivation The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel cor...
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Published in | Bioinformatics Vol. 36; no. 24; pp. 5703 - 5705 |
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Main Authors | , , , , , , , , , , , , |
Format | Journal Article Web Resource |
Language | English |
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England
Oxford University Press
05.04.2021
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Abstract | Abstract
Motivation
The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development.
Results
The ontology comprises 2270 classes of concepts and 38 987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19. The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute.
Availabilityand implementation
COVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19.
Supplementary information
Supplementary data are available at Bioinformatics online. |
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AbstractList | Abstract
Motivation
The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development.
Results
The ontology comprises 2270 classes of concepts and 38 987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19. The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute.
Availabilityand implementation
COVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19.
Supplementary information
Supplementary data are available at Bioinformatics online. The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development. The ontology comprises 2270 classes of concepts and 38 987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19. The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute. COVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19. Supplementary data are available at Bioinformatics online. |
Author | DeLong, Lauren Nicole Kodamullil, Alpha Tom Keminer, Oliver Gebel, Stephan Kohler, Manfred Madan, Sumit Balabin, Helena Hofmann-Apitius, Martin Baksi, Shounak Jose, Geena Mariya Sargsyan, Astghik Jacobs, Marc Darms, Johannes |
AuthorAffiliation | btaa1057-aff1 Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) , 53754 Sankt Augustin, Germany btaa1057-aff3 Causality Biomodels, Kinfra Hi-Tech Park , Cochin, Kerala 683503, India btaa1057-aff2 Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn , 53113 Bonn, Germany btaa1057-aff4 Fraunhofer Institute for Molecular Biology and Applied Ecology-ScreeningPort , Hamburg, Germany |
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Cites_doi | 10.1002/minf.202000028 10.1007/s40484-020-0199-0 10.1097/CM9.0000000000000797 |
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References | Zhao (2023062408125196700_btaa1057-B7) 2020; 8 (2023062408125196700_btaa1057-B1) 2020 Jin (2023062408125196700_btaa1057-B3) 2020 Smith (2023062408125196700_btaa1057-B5) 2020 Fan (2023062408125196700_btaa1057-B2) 2020 Kucharski (2023062408125196700_btaa1057-B4) 2020 Ton (2023062408125196700_btaa1057-B6) 2020; 39 |
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Snippet | Abstract
Motivation
The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches... The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data... |
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