PT  - JOURNAL ARTICLE
AU  - Daniel Domingo-Fernández
AU  - Shounak Baksi
AU  - Bruce Schultz
AU  - Yojana Gadiya
AU  - Reagon Karki
AU  - Tamara Raschka
AU  - Christian Ebeling
AU  - Martin Hofmann-Apitius
AU  - Alpha Tom Kodamullil
TI  - COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology
AID  - 10.1101/2020.04.14.040667
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.04.14.040667
4099  - http://biorxiv.org/content/early/2020/04/15/2020.04.14.040667.short
4100  - http://biorxiv.org/content/early/2020/04/15/2020.04.14.040667.full
AB  - Summary The past few weeks have witnessed a worldwide mobilization of the research community in response to the novel coronavirus (COVID-19). This global response has led to a burst of publications on the pathophysiology of the virus, yet 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 The COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de.Contact alpha.tom.kodamullil{at}scai.fraunhofer.deSupplementary information Supplementary data are available online.Competing Interest StatementThe authors have declared no competing interest.