COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology.
Bioinformatics
; 37(9): 1332-1334, 2021 06 09.
Article
in English
| MEDLINE | ID: covidwho-795009
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Software
/
COVID-19
Type of study:
Experimental Studies
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
Bioinformatics
Journal subject:
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Bioinformatics
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