About: Drug repurposing prediction for COVID-19 using probabilistic networks and crowdsourced curation

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Drug repurposing prediction for COVID-19 using probabilistic networks and crowdsourced curation
Creator	Alsobhe, Aoesha Anastasi, Elisa Atallah, Christian Bird, Jasmine Brown, Bradley Didon, Dwayne Gater, Phoenix James, Katherine Lennon, David Mclaughlin, James Moreland, Pollyanna Pocock, Matthew Skelton, David Whitaker, Caroline Wipat, Anil
source	ArXiv
abstract	Severe acute respiratory syndrome coronavirus two (SARS-CoV-2), the virus responsible for the coronavirus disease 2019 (COVID-19) pandemic, represents an unprecedented global health challenge. Consequently, a large amount of research into the disease pathogenesis and potential treatments has been carried out in a short time frame. However, developing novel drugs is a costly and lengthy process, and is unlikely to deliver a timely treatment for the pandemic. Drug repurposing, by contrast, provides an attractive alternative, as existing drugs have already undergone many of the regulatory requirements. In this work we used a combination of network algorithms and human curation to search integrated knowledge graphs, identifying drug repurposing opportunities for COVID-19. We demonstrate the value of this approach, reporting on eight potential repurposing opportunities identified, and discuss how this approach could be incorporated into future studies.
has issue date	2020-05-22(xsd:dateTime)
has license	arxiv
sha1sum (hex)	3ccaa465d3a20adea09d6fc496c7bd9cdd0fdd9a
resource representing a document's title	Drug repurposing prediction for COVID-19 using probabilistic networks and crowdsourced curation
resource representing a document's body	covid:3ccaa465d3a20adea09d6fc496c7bd9cdd0fdd9a#body_text
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