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Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies
SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been ubiquitously used in an attempt to timely and effectively cope with the resulting global health crisis. In order to extensively assess such contribution, we collected, categorized and prioritized over...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8689952/ https://www.ncbi.nlm.nih.gov/pubmed/34788381 http://dx.doi.org/10.1093/bib/bbab456 |
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author | Napolitano, Francesco Xu, Xiaopeng Gao, Xin |
author_facet | Napolitano, Francesco Xu, Xiaopeng Gao, Xin |
author_sort | Napolitano, Francesco |
collection | PubMed |
description | SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been ubiquitously used in an attempt to timely and effectively cope with the resulting global health crisis. In order to extensively assess such contribution, we collected, categorized and prioritized over 17 000 COVID-19-related research articles including both peer-reviewed and preprint publications that make a relevant use of computational approaches. Using machine learning methods, we identified six broad application areas i.e. Molecular Pharmacology and Biomarkers, Molecular Virology, Epidemiology, Healthcare, Clinical Medicine and Clinical Imaging. We then used our prioritization model as a guidance through an extensive, systematic review of the most relevant studies. We believe that the remarkable contribution provided by computational applications during the ongoing pandemic motivates additional efforts toward their further development and adoption, with the aim of enhancing preparedness and critical response for current and future emergencies. |
format | Online Article Text |
id | pubmed-8689952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-86899522022-01-05 Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies Napolitano, Francesco Xu, Xiaopeng Gao, Xin Brief Bioinform Review SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been ubiquitously used in an attempt to timely and effectively cope with the resulting global health crisis. In order to extensively assess such contribution, we collected, categorized and prioritized over 17 000 COVID-19-related research articles including both peer-reviewed and preprint publications that make a relevant use of computational approaches. Using machine learning methods, we identified six broad application areas i.e. Molecular Pharmacology and Biomarkers, Molecular Virology, Epidemiology, Healthcare, Clinical Medicine and Clinical Imaging. We then used our prioritization model as a guidance through an extensive, systematic review of the most relevant studies. We believe that the remarkable contribution provided by computational applications during the ongoing pandemic motivates additional efforts toward their further development and adoption, with the aim of enhancing preparedness and critical response for current and future emergencies. Oxford University Press 2021-11-11 /pmc/articles/PMC8689952/ /pubmed/34788381 http://dx.doi.org/10.1093/bib/bbab456 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Review Napolitano, Francesco Xu, Xiaopeng Gao, Xin Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies |
title | Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies |
title_full | Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies |
title_fullStr | Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies |
title_full_unstemmed | Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies |
title_short | Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies |
title_sort | impact of computational approaches in the fight against covid-19: an ai guided review of 17 000 studies |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8689952/ https://www.ncbi.nlm.nih.gov/pubmed/34788381 http://dx.doi.org/10.1093/bib/bbab456 |
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