Apr 15, 2020

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

BioRxiv : the Preprint Server for Biology
D. Domingo-FernandezAlpha Tom Kodamullil

Abstract

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.

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Mentioned in this Paper

Study
Laboratory Procedures
Genome
Genes
Regulation of Biological Process
Transcription Initiation Site
Transcription, Genetic
Histocompatibility Testing
Gene Expression
Caged molecule

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