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COVID-19 Knowledge Graph: A computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology

 
: Domingo-Fernández, Daniel; Baksi, Shounak; Schultz, Bruce; Gadiya, Yojana; Kark, Reagon; Raschka, Tamara; Ebeling, Christian; Hofmann-Apitius, Martin; Kodamullil, Alpha Tom

:
Volltext urn:nbn:de:0011-n-6032963 (238 KByte PDF)
MD5 Fingerprint: d6d421ee8b99ced0cbfae66df05fda3b
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Erstellt am: 9.10.2020


Bioinformatics (2020), Online First, Art. btaa834, 3 S.
ISSN: 1367-4803
ISSN: 1460-2059
ISSN: 1367-4811
Fraunhofer-Gesellschaft FhG
MAVO; Human pharmacome
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer SCAI ()
Knowledge Graphs; COVID-19; cause-and-effect models; bioinformatic; knowledge-driven analysis

Abstract
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.

: http://publica.fraunhofer.de/dokumente/N-603296.html