Predicting the secondary structure of proteins using Machine Learning algorithms

Detalhes bibliográficos
Autor(a) principal: Rui Camacho
Data de Publicação: 2012
Outros Autores: Rita Ferreira, Natacha Rosa, Vânia Guimarães, Nuno A Fonseca, Vítor Santos Costa, Miguel de Sousa, Alexandre Magalhaes
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://repositorio-aberto.up.pt/handle/10216/67119
Resumo: The functions of proteins in living organisms are related to their 3-D structure, which is known to be ultimately determined by their linear sequence of amino acids that together form these macromolecules. It is, therefore, of great importance to be able to understand and predict how the protein 3D-structure arises from a particular linear sequence of amino acids. In this paper we report the application of Machine Learning methods to predict, with high values of accuracy, the secondary structure of proteins, namely alpha-helices and beta-sheets, which are intermediate levels of the local structure.
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spelling Predicting the secondary structure of proteins using Machine Learning algorithmsMatemáticaMathematicsThe functions of proteins in living organisms are related to their 3-D structure, which is known to be ultimately determined by their linear sequence of amino acids that together form these macromolecules. It is, therefore, of great importance to be able to understand and predict how the protein 3D-structure arises from a particular linear sequence of amino acids. In this paper we report the application of Machine Learning methods to predict, with high values of accuracy, the secondary structure of proteins, namely alpha-helices and beta-sheets, which are intermediate levels of the local structure.20122012-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://repositorio-aberto.up.pt/handle/10216/67119eng1748-567310.1504/ijdmb.2012.050265Rui CamachoRita FerreiraNatacha RosaVânia GuimarãesNuno A FonsecaVítor Santos CostaMiguel de SousaAlexandre Magalhaesinfo: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:RCAAP2023-11-29T16:09:35Zoai:repositorio-aberto.up.pt:10216/67119Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:38:21.590335Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Predicting the secondary structure of proteins using Machine Learning algorithms
title Predicting the secondary structure of proteins using Machine Learning algorithms
spellingShingle Predicting the secondary structure of proteins using Machine Learning algorithms
Rui Camacho
Matemática
Mathematics
title_short Predicting the secondary structure of proteins using Machine Learning algorithms
title_full Predicting the secondary structure of proteins using Machine Learning algorithms
title_fullStr Predicting the secondary structure of proteins using Machine Learning algorithms
title_full_unstemmed Predicting the secondary structure of proteins using Machine Learning algorithms
title_sort Predicting the secondary structure of proteins using Machine Learning algorithms
author Rui Camacho
author_facet Rui Camacho
Rita Ferreira
Natacha Rosa
Vânia Guimarães
Nuno A Fonseca
Vítor Santos Costa
Miguel de Sousa
Alexandre Magalhaes
author_role author
author2 Rita Ferreira
Natacha Rosa
Vânia Guimarães
Nuno A Fonseca
Vítor Santos Costa
Miguel de Sousa
Alexandre Magalhaes
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Rui Camacho
Rita Ferreira
Natacha Rosa
Vânia Guimarães
Nuno A Fonseca
Vítor Santos Costa
Miguel de Sousa
Alexandre Magalhaes
dc.subject.por.fl_str_mv Matemática
Mathematics
topic Matemática
Mathematics
description The functions of proteins in living organisms are related to their 3-D structure, which is known to be ultimately determined by their linear sequence of amino acids that together form these macromolecules. It is, therefore, of great importance to be able to understand and predict how the protein 3D-structure arises from a particular linear sequence of amino acids. In this paper we report the application of Machine Learning methods to predict, with high values of accuracy, the secondary structure of proteins, namely alpha-helices and beta-sheets, which are intermediate levels of the local structure.
publishDate 2012
dc.date.none.fl_str_mv 2012
2012-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://repositorio-aberto.up.pt/handle/10216/67119
url https://repositorio-aberto.up.pt/handle/10216/67119
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1748-5673
10.1504/ijdmb.2012.050265
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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