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Discovering trends and hotspots of biosafety and biosecurity research via machine learning

Coronavirus disease 2019 (COVID-19) has infected hundreds of millions of people and killed millions of them. As an RNA virus, COVID-19 is more susceptible to variation than other viruses. Many problems involved in this epidemic have made biosafety and biosecurity (hereafter collectively referred to...

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Autores principales: Guan, Renchu, Pang, Haoyu, Liang, Yanchun, Shao, Zhongjun, Gao, Xin, Xu, Dong, Feng, Xiaoyue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9487701/
https://www.ncbi.nlm.nih.gov/pubmed/35596953
http://dx.doi.org/10.1093/bib/bbac194
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author Guan, Renchu
Pang, Haoyu
Liang, Yanchun
Shao, Zhongjun
Gao, Xin
Xu, Dong
Feng, Xiaoyue
author_facet Guan, Renchu
Pang, Haoyu
Liang, Yanchun
Shao, Zhongjun
Gao, Xin
Xu, Dong
Feng, Xiaoyue
author_sort Guan, Renchu
collection PubMed
description Coronavirus disease 2019 (COVID-19) has infected hundreds of millions of people and killed millions of them. As an RNA virus, COVID-19 is more susceptible to variation than other viruses. Many problems involved in this epidemic have made biosafety and biosecurity (hereafter collectively referred to as ‘biosafety’) a popular and timely topic globally. Biosafety research covers a broad and diverse range of topics, and it is important to quickly identify hotspots and trends in biosafety research through big data analysis. However, the data-driven literature on biosafety research discovery is quite scant. We developed a novel topic model based on latent Dirichlet allocation, affinity propagation clustering and the PageRank algorithm (LDAPR) to extract knowledge from biosafety research publications from 2011 to 2020. Then, we conducted hotspot and trend analysis with LDAPR and carried out further studies, including annual hot topic extraction, a 10-year keyword evolution trend analysis, topic map construction, hot region discovery and fine-grained correlation analysis of interdisciplinary research topic trends. These analyses revealed valuable information that can guide epidemic prevention work: (1) the research enthusiasm over a certain infectious disease not only is related to its epidemic characteristics but also is affected by the progress of research on other diseases, and (2) infectious diseases are not only strongly related to their corresponding microorganisms but also potentially related to other specific microorganisms. The detailed experimental results and our code are available at https://github.com/KEAML-JLU/Biosafety-analysis.
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spelling pubmed-94877012022-09-21 Discovering trends and hotspots of biosafety and biosecurity research via machine learning Guan, Renchu Pang, Haoyu Liang, Yanchun Shao, Zhongjun Gao, Xin Xu, Dong Feng, Xiaoyue Brief Bioinform Problem Solving Protocol Coronavirus disease 2019 (COVID-19) has infected hundreds of millions of people and killed millions of them. As an RNA virus, COVID-19 is more susceptible to variation than other viruses. Many problems involved in this epidemic have made biosafety and biosecurity (hereafter collectively referred to as ‘biosafety’) a popular and timely topic globally. Biosafety research covers a broad and diverse range of topics, and it is important to quickly identify hotspots and trends in biosafety research through big data analysis. However, the data-driven literature on biosafety research discovery is quite scant. We developed a novel topic model based on latent Dirichlet allocation, affinity propagation clustering and the PageRank algorithm (LDAPR) to extract knowledge from biosafety research publications from 2011 to 2020. Then, we conducted hotspot and trend analysis with LDAPR and carried out further studies, including annual hot topic extraction, a 10-year keyword evolution trend analysis, topic map construction, hot region discovery and fine-grained correlation analysis of interdisciplinary research topic trends. These analyses revealed valuable information that can guide epidemic prevention work: (1) the research enthusiasm over a certain infectious disease not only is related to its epidemic characteristics but also is affected by the progress of research on other diseases, and (2) infectious diseases are not only strongly related to their corresponding microorganisms but also potentially related to other specific microorganisms. The detailed experimental results and our code are available at https://github.com/KEAML-JLU/Biosafety-analysis. Oxford University Press 2022-05-22 /pmc/articles/PMC9487701/ /pubmed/35596953 http://dx.doi.org/10.1093/bib/bbac194 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Problem Solving Protocol
Guan, Renchu
Pang, Haoyu
Liang, Yanchun
Shao, Zhongjun
Gao, Xin
Xu, Dong
Feng, Xiaoyue
Discovering trends and hotspots of biosafety and biosecurity research via machine learning
title Discovering trends and hotspots of biosafety and biosecurity research via machine learning
title_full Discovering trends and hotspots of biosafety and biosecurity research via machine learning
title_fullStr Discovering trends and hotspots of biosafety and biosecurity research via machine learning
title_full_unstemmed Discovering trends and hotspots of biosafety and biosecurity research via machine learning
title_short Discovering trends and hotspots of biosafety and biosecurity research via machine learning
title_sort discovering trends and hotspots of biosafety and biosecurity research via machine learning
topic Problem Solving Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9487701/
https://www.ncbi.nlm.nih.gov/pubmed/35596953
http://dx.doi.org/10.1093/bib/bbac194
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