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1000 Titel
  • Analysis of COVID-19 clinical trials: A data-driven, ontology-based, and natural language processing approach
1000 Autor/in
  1. Alag, Shray |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-09-30
1000 Erschienen in
1000 Quellenangabe
  • 15(9):e0239694
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1371/journal.pone.0239694 |
1000 Ergänzendes Material
  • https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0239694#sec017 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • With the novel COVID-19 pandemic disrupting and threatening the lives of millions, researchers and clinicians have been recently conducting clinical trials at an unprecedented rate to learn more about the virus and potential drugs/treatments/vaccines to treat its infection. As a result of the influx of clinical trials, researchers, clinicians, and the lay public, now more than ever, face a significant challenge in keeping up-to-date with the rapid rate of discoveries and advances. To remedy this problem, this research mined the ClinicalTrials.gov corpus to extract COVID-19 related clinical trials, produce unique reports to summarize findings and make the meta-data available via Application Programming Interfaces (APIs). Unique reports were created for each drug/intervention, Medical Subject Heading (MeSH) term, and Human Phenotype Ontology (HPO) term. These reports, which have been run over multiple time points, along with APIs to access meta-data, are freely available at http://covidresearchtrials.com. The pipeline, reports, association of COVID-19 clinical trials with MeSH and HPO terms, insights, public repository, APIs, and correlations produced are all novel in this work. The freely available, novel resources present up-to-date relevant biological information and insights in a robust, accessible manner, illustrating their invaluable potential to aid researchers overcome COVID-19 and save hundreds of thousands of lives.
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal Clinical trials
lokal Java
lokal Respiratory infections
lokal Pneumonia
lokal SARS
lokal Lung and intrathoracic tumors
lokal Respiratory physiology
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-1725-2891
1000 Label
1000 Fördernummer
  1. -
1000 Förderprogramm
  1. -
1000 Dateien
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6427244.rdf
1000 Erstellt am 2021-05-05T09:33:21.669+0200
1000 Erstellt von 5
1000 beschreibt frl:6427244
1000 Bearbeitet von 25
1000 Zuletzt bearbeitet 2021-06-04T09:19:14.453+0200
1000 Objekt bearb. Wed May 05 11:10:48 CEST 2021
1000 Vgl. frl:6427244
1000 Oai Id
  1. oai:frl.publisso.de:frl:6427244 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
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