About: Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS

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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	Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS
Creator	Zhang, Xiaolu Meller, Jarek Mccullumsmith, Robert O'donovan, Sinead Wu, Xiaojun Taylor, R Eby, Hunter Alnafisah, Rawan Asah, Sophie Creeden, Justin Educorresponding Author, Robert Henkel, Nicholas Imami, Ali Reigle, James Thorman, Alexander
source	Medline; PMC
abstract	The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. As no vaccine or drugs are currently approved to specifically treat COVID-19, identification of effective therapeutics is crucial to treat the afflicted and limit disease spread. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an “omics” repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and signatures of coronavirus-infected cell lines to identify therapeutics with concordant signatures and discordant signatures, respectively. Our findings include three FDA approved drugs that have established antiviral activity, including protein kinase inhibitors, providing a promising new category of candidates for COVID-19 interventions.
has issue date	2020-04-30(xsd:dateTime)
bibo:doi	10.21203/rs.3.rs-25643/v1
bibo:pmid	32702077
has license	cc-by
sha1sum (hex)	74cc85d081da2286c5a4e27f7eb5dadb4575519c
schema:url	https://doi.org/10.21203/rs.3.rs-25643/v1
resource representing a document's title	Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS
has PubMed Central identifier	PMC7336712
has PubMed identifier	32702077
schema:publication	Res Sq
resource representing a document's body	covid:74cc85d081da2286c5a4e27f7eb5dadb4575519c#body_text
is schema:about of	named entity 'caused' named entity 'candidates' named entity 'discordant' named entity 'Signatures' named entity 'limit' named entity 'promising' named entity 'therapeutics' named entity 'FDA' named entity 'PROBED' named entity 'INFECTED CELL' named entity 'VACCINE' named entity 'CAUSED BY' named entity 'CORONAVIRUSES' named entity 'CELLULAR' named entity 'CONCORDANT' named entity 'LIBRARY' named entity 'EFFECTIVE' named entity 'PROTEIN KINASE INHIBITORS' named entity 'SIGNATURES' named entity 'CANDIDATE' named entity 'APPROVED' covid:arg/74cc85d081da2286c5a4e27f7eb5dadb4575519c named entity 'Integrated' named entity 'Cellular' named entity 'COVID-19' named entity 'antiviral' named entity 'identification' named entity 'effective' named entity 'candidate' named entity 'Pharmacodynamics' named entity 'FDA' named entity 'COVID' named entity 'protein kinase inhibitors' named entity 'cell lines' named entity 'antiviral activity' named entity 'SARS-CoV-2' named entity 'COVID' named entity 'vaccine' named entity 'Network-Based' named entity 'coronavirus' named entity 'COVID' named entity 'PubMed' named entity 'hypoxemic' named entity 'transcriptional activation' named entity 'SARS' named entity 'COVID-19' named entity 'drug target' named entity 'Gene Ontology' named entity 'Gene Ontology' named entity 'antiviral' named entity 'kinase' named entity 'Influenza' named entity 'virus' named entity 'pathogens' named entity 'kinase' named entity 'COVID' named entity 'differential gene expression' named entity 'custom script' named entity 'antiviral' named entity 'antiparasitic' named entity 'differential gene expression' named entity 'drug target' named entity 'pathogen' named entity 'cross-platform' named entity 'transcriptome' named entity 'complement' named entity 'COVID'

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