RT Journal Article
SR Electronic
T1 COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.04.14.040667
DO 10.1101/2020.04.14.040667
A1 Daniel Domingo-Fernández
A1 Shounak Baksi
A1 Bruce Schultz
A1 Yojana Gadiya
A1 Reagon Karki
A1 Tamara Raschka
A1 Christian Ebeling
A1 Martin Hofmann-Apitius
A1 Alpha Tom Kodamullil
YR 2020
UL http://biorxiv.org/content/early/2020/04/15/2020.04.14.040667.abstract
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.