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Analysis of COVID-19 clinical trials: A data-driven, ontology-based, and natural language processing approach
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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526926/ https://www.ncbi.nlm.nih.gov/pubmed/32997699 http://dx.doi.org/10.1371/journal.pone.0239694 |
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author | Alag, Shray |
author_facet | Alag, Shray |
author_sort | Alag, Shray |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7526926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-75269262020-10-06 Analysis of COVID-19 clinical trials: A data-driven, ontology-based, and natural language processing approach Alag, Shray PLoS One Research Article 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. Public Library of Science 2020-09-30 /pmc/articles/PMC7526926/ /pubmed/32997699 http://dx.doi.org/10.1371/journal.pone.0239694 Text en © 2020 Shray Alag http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Alag, Shray Analysis of COVID-19 clinical trials: A data-driven, ontology-based, and natural language processing approach |
title | Analysis of COVID-19 clinical trials: A data-driven, ontology-based, and natural language processing approach |
title_full | Analysis of COVID-19 clinical trials: A data-driven, ontology-based, and natural language processing approach |
title_fullStr | Analysis of COVID-19 clinical trials: A data-driven, ontology-based, and natural language processing approach |
title_full_unstemmed | Analysis of COVID-19 clinical trials: A data-driven, ontology-based, and natural language processing approach |
title_short | Analysis of COVID-19 clinical trials: A data-driven, ontology-based, and natural language processing approach |
title_sort | analysis of covid-19 clinical trials: a data-driven, ontology-based, and natural language processing approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526926/ https://www.ncbi.nlm.nih.gov/pubmed/32997699 http://dx.doi.org/10.1371/journal.pone.0239694 |
work_keys_str_mv | AT alagshray analysisofcovid19clinicaltrialsadatadrivenontologybasedandnaturallanguageprocessingapproach |