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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:PloS one Ročník 15; číslo 9; s. e0239694
Hlavní autor: Alag, Shray
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 30.09.2020
Public Library of Science (PLoS)
Témata:
API (Computer programming)
Betacoronavirus
Biological Ontologies
Biology and Life Sciences
Clinical trials
Clinical Trials as Topic
Computational Biology
Coronavirus Infections - therapy
Coronaviruses
COVID-19
Disease transmission
Drugs
Epidemics
Extensible Markup Language
Genotype & phenotype
Humans
Interfaces
Internet
Intervention
Java
Medical research
Medical Subject Headings
Medical Subject Headings-MeSH
Medicine and Health Sciences
Methods
Mutation
Natural Language Processing
Ontology
Pandemics
People and Places
Phenotype
Phenotypes
Pneumonia, Viral - therapy
Proteins
Research and Analysis Methods
SARS-CoV-2
Secondary data analysis
Severe acute respiratory syndrome coronavirus 2
Software
Trends
Uniqueness
United States
Vaccines
Viral diseases
Viruses
United States
United States > US
ISSN:1932-6203, 1932-6203
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Competing Interests: The author has declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0239694