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Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics

The COVID-19 pandemic constitutes an ongoing worldwide threat to human society and has caused massive impacts on global public health, the economy and the political landscape. The key to gaining control of the disease lies in understanding the genetics of SARS-CoV-2 and the disease spectrum that fol...

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Autores principales: Wu, Mengjia, Zhang, Yi, Grosser, Mark, Tipper, Steven, Venter, Deon, Lin, Hua, Lu, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184093/
https://www.ncbi.nlm.nih.gov/pubmed/34109284
http://dx.doi.org/10.3389/frma.2021.683212
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author Wu, Mengjia
Zhang, Yi
Grosser, Mark
Tipper, Steven
Venter, Deon
Lin, Hua
Lu, Jie
author_facet Wu, Mengjia
Zhang, Yi
Grosser, Mark
Tipper, Steven
Venter, Deon
Lin, Hua
Lu, Jie
author_sort Wu, Mengjia
collection PubMed
description The COVID-19 pandemic constitutes an ongoing worldwide threat to human society and has caused massive impacts on global public health, the economy and the political landscape. The key to gaining control of the disease lies in understanding the genetics of SARS-CoV-2 and the disease spectrum that follows infection. This study leverages traditional and intelligent bibliometric methods to conduct a multi-dimensional analysis on 5,632 COVID-19 genetic research papers, revealing that 1) the key players include research institutions from the United States, China, Britain and Canada; 2) research topics predominantly focus on virus infection mechanisms, virus testing, gene expression related to the immune reactions and patient clinical manifestation; 3) studies originated from the comparison of SARS-CoV-2 to previous human coronaviruses, following which research directions diverge into the analysis of virus molecular structure and genetics, the human immune response, vaccine development and gene expression related to immune responses; and 4) genes that are frequently highlighted include ACE2, IL6, TMPRSS2, and TNF. Emerging genes to the COVID-19 consist of FURIN, CXCL10, OAS1, OAS2, OAS3, and ISG15. This study demonstrates that our suite of novel bibliometric tools could help biomedical researchers follow this rapidly growing field and provide substantial evidence for policymakers’ decision-making on science policy and public health administration.
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spelling pubmed-81840932021-06-08 Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics Wu, Mengjia Zhang, Yi Grosser, Mark Tipper, Steven Venter, Deon Lin, Hua Lu, Jie Front Res Metr Anal Research Metrics and Analytics The COVID-19 pandemic constitutes an ongoing worldwide threat to human society and has caused massive impacts on global public health, the economy and the political landscape. The key to gaining control of the disease lies in understanding the genetics of SARS-CoV-2 and the disease spectrum that follows infection. This study leverages traditional and intelligent bibliometric methods to conduct a multi-dimensional analysis on 5,632 COVID-19 genetic research papers, revealing that 1) the key players include research institutions from the United States, China, Britain and Canada; 2) research topics predominantly focus on virus infection mechanisms, virus testing, gene expression related to the immune reactions and patient clinical manifestation; 3) studies originated from the comparison of SARS-CoV-2 to previous human coronaviruses, following which research directions diverge into the analysis of virus molecular structure and genetics, the human immune response, vaccine development and gene expression related to immune responses; and 4) genes that are frequently highlighted include ACE2, IL6, TMPRSS2, and TNF. Emerging genes to the COVID-19 consist of FURIN, CXCL10, OAS1, OAS2, OAS3, and ISG15. This study demonstrates that our suite of novel bibliometric tools could help biomedical researchers follow this rapidly growing field and provide substantial evidence for policymakers’ decision-making on science policy and public health administration. Frontiers Media S.A. 2021-05-24 /pmc/articles/PMC8184093/ /pubmed/34109284 http://dx.doi.org/10.3389/frma.2021.683212 Text en Copyright © 2021 Wu, Zhang, Grosser, Tipper, Venter, Lin and Lu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Research Metrics and Analytics
Wu, Mengjia
Zhang, Yi
Grosser, Mark
Tipper, Steven
Venter, Deon
Lin, Hua
Lu, Jie
Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics
title Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics
title_full Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics
title_fullStr Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics
title_full_unstemmed Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics
title_short Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics
title_sort profiling covid-19 genetic research: a data-driven study utilizing intelligent bibliometrics
topic Research Metrics and Analytics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184093/
https://www.ncbi.nlm.nih.gov/pubmed/34109284
http://dx.doi.org/10.3389/frma.2021.683212
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