Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theories

Detalhes bibliográficos
Autor(a) principal: Machado, J. A. Tenreiro
Data de Publicação: 2020
Outros Autores: Rocha-Neves, João M., Andrade, José P.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/19414
Resumo: This paper tackles the information of 133 RNA viruses available in public databases under the light of several mathematical and computational tools. First, the formal concepts of distance metrics, Kolmogorov complexity and Shannon information are recalled. Second, the computational tools available presently for tackling and visualizing patterns embedded in datasets, such as the hierarchical clustering and the multidimensional scaling, are discussed. The synergies of the common application of the mathematical and computational resources are then used for exploring the RNA data, cross-evaluating the normalized compression distance, entropy and Jensen–Shannon divergence, versus representations in two and three dimensions. The results of these different perspectives give extra light in what concerns the relations between the distinct RNA viruses.
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spelling Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theoriesCOVID-19Kolmogorov complexity theoryShannon information theoryHierarchical clusteringMultidimensional scalingThis paper tackles the information of 133 RNA viruses available in public databases under the light of several mathematical and computational tools. First, the formal concepts of distance metrics, Kolmogorov complexity and Shannon information are recalled. Second, the computational tools available presently for tackling and visualizing patterns embedded in datasets, such as the hierarchical clustering and the multidimensional scaling, are discussed. The synergies of the common application of the mathematical and computational resources are then used for exploring the RNA data, cross-evaluating the normalized compression distance, entropy and Jensen–Shannon divergence, versus representations in two and three dimensions. The results of these different perspectives give extra light in what concerns the relations between the distinct RNA viruses.The authors thank all those who have contributed and shared sequences to the GISAID database (https://www.gisaid.org/). The authors also thank those who have contributed to the GenBank of the National Center for Biotechnology Information (NCBI) databases (https://www.ncbi.nlm.nih.gov/genbank). The authors also thank Rómulo Antão for the help in handling the information with the compressors zlib and bz2.SpringerRepositório Científico do Instituto Politécnico do PortoMachado, J. A. TenreiroRocha-Neves, João M.Andrade, José P.2022-01-12T11:06:08Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdftext/plain; charset=utf-8http://hdl.handle.net/10400.22/19414eng10.1007/s11071-020-05771-8metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-04-19T01:46:55ZPortal AgregadorONG
dc.title.none.fl_str_mv Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theories
title Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theories
spellingShingle Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theories
Machado, J. A. Tenreiro
COVID-19
Kolmogorov complexity theory
Shannon information theory
Hierarchical clustering
Multidimensional scaling
title_short Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theories
title_full Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theories
title_fullStr Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theories
title_full_unstemmed Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theories
title_sort Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theories
author Machado, J. A. Tenreiro
author_facet Machado, J. A. Tenreiro
Rocha-Neves, João M.
Andrade, José P.
author_role author
author2 Rocha-Neves, João M.
Andrade, José P.
author2_role author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Machado, J. A. Tenreiro
Rocha-Neves, João M.
Andrade, José P.
dc.subject.por.fl_str_mv COVID-19
Kolmogorov complexity theory
Shannon information theory
Hierarchical clustering
Multidimensional scaling
topic COVID-19
Kolmogorov complexity theory
Shannon information theory
Hierarchical clustering
Multidimensional scaling
description This paper tackles the information of 133 RNA viruses available in public databases under the light of several mathematical and computational tools. First, the formal concepts of distance metrics, Kolmogorov complexity and Shannon information are recalled. Second, the computational tools available presently for tackling and visualizing patterns embedded in datasets, such as the hierarchical clustering and the multidimensional scaling, are discussed. The synergies of the common application of the mathematical and computational resources are then used for exploring the RNA data, cross-evaluating the normalized compression distance, entropy and Jensen–Shannon divergence, versus representations in two and three dimensions. The results of these different perspectives give extra light in what concerns the relations between the distinct RNA viruses.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-01-01T00:00:00Z
2022-01-12T11:06:08Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/19414
url http://hdl.handle.net/10400.22/19414
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1007/s11071-020-05771-8
dc.rights.driver.fl_str_mv metadata only access
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rights_invalid_str_mv metadata only access
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
text/plain; charset=utf-8
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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