COVID-19 Knowledge Graph : a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology
© The Author(s) 2020. Published by Oxford University Press..
SUMMARY: The COVID-19 crisis has elicited a global response by the scientific community that has led to a burst of publications on the pathophysiology of the virus. However, without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats.
AVAILABILITY AND IMPLEMENTATION: The COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:37 |
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| Enthalten in: |
Bioinformatics (Oxford, England) - 37(2021), 9 vom: 09. Juni, Seite 1332-1334 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Domingo-Fernández, Daniel [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 14.06.2021 Date Revised 19.05.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/bioinformatics/btaa834 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM315473312 |
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| 245 | 1 | 0 | |a COVID-19 Knowledge Graph |b a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology |
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| 500 | |a Date Revised 19.05.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2020. Published by Oxford University Press. | ||
| 520 | |a SUMMARY: The COVID-19 crisis has elicited a global response by the scientific community that has led to a burst of publications on the pathophysiology of the virus. However, without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats | ||
| 520 | |a AVAILABILITY AND IMPLEMENTATION: The COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de | ||
| 520 | |a SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Baksi, Shounak |e verfasserin |4 aut | |
| 700 | 1 | |a Schultz, Bruce |e verfasserin |4 aut | |
| 700 | 1 | |a Gadiya, Yojana |e verfasserin |4 aut | |
| 700 | 1 | |a Karki, Reagon |e verfasserin |4 aut | |
| 700 | 1 | |a Raschka, Tamara |e verfasserin |4 aut | |
| 700 | 1 | |a Ebeling, Christian |e verfasserin |4 aut | |
| 700 | 1 | |a Hofmann-Apitius, Martin |e verfasserin |4 aut | |
| 700 | 1 | |a Kodamullil, Alpha Tom |e verfasserin |4 aut | |
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