About: Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning

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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
has title	Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning
Creator	Zhang, Zheng Cheng, Feixiong Hou, Yuan Zhou, Yadi Karypis, George Song, Xiang Li, Kenli Ma, Tengfei Pan, Xiaoqin Zeng, Xiangxiang
Source	Medline; PMC
abstract	[Image: see text] There have been more than 2.2 million confirmed cases and over 120 000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there is currently a lack of proven effective medications against COVID-19. Drug repurposing offers a promising route for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep-learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression from a large scientific corpus of 24 million PubMed publications. Using Amazon’s AWS computing resources and a network-based, deep-learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic associations with COVID-19 were validated by transcriptomic and proteomics data in SARS-CoV-2-infected human cells and data from ongoing clinical trials. Whereas this study by no means recommends specific drugs, it demonstrates a powerful deep-learning methodology to prioritize existing drugs for further investigation, which holds the potential to accelerate therapeutic development for COVID-19.
has issue date	2020-07-12(xsd:dateTime)
bibo:doi	10.1021/acs.jproteome.0c00316
bibo:pmid	32654489
has license	no-cc
sha1sum (hex)	a4ce5b42f3e41611b3977a58e667668efc53a627
schema:url	https://doi.org/10.1021/acs.jproteome.0c00316
resource representing a document's title	Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning
has PubMed Central identifier	PMC7384389
has PubMed identifier	32654489
schema:publication	J Proteome Res
resource representing a document's body	covid:a4ce5b42f3e41611b3977a58e667668efc53a627#body_text
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