Predicting the secondary structure of proteins using Machine Learning algorithms

Detalhes bibliográficos
Autor(a) principal: Nuno Fonseca
Data de Publicação: 2012
Outros Autores: Vítor Santos Costa, Alexandre Magalhães, Miguel de Sousa, Vânia Guimarães, Natacha Rosa, Rui Camacho, Rita Ferreira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/2925
http://dx.doi.org/10.1504/IJDMB.2012.050265
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 α-helices and ß-sheets, which are intermediate levels of the local structure.
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spelling Predicting the secondary structure of proteins using Machine Learning algorithmsThe 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 α-helices and ß-sheets, which are intermediate levels of the local structure.2017-11-16T14:18:44Z2012-01-01T00:00:00Z2012info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/2925http://dx.doi.org/10.1504/IJDMB.2012.050265engNuno FonsecaVítor Santos CostaAlexandre MagalhãesMiguel de SousaVânia GuimarãesNatacha RosaRui CamachoRita Ferreirainfo: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:RCAAP2023-05-15T10:20:23Zoai:repositorio.inesctec.pt:123456789/2925Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:03.047702Repositório 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
Nuno Fonseca
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 Nuno Fonseca
author_facet Nuno Fonseca
Vítor Santos Costa
Alexandre Magalhães
Miguel de Sousa
Vânia Guimarães
Natacha Rosa
Rui Camacho
Rita Ferreira
author_role author
author2 Vítor Santos Costa
Alexandre Magalhães
Miguel de Sousa
Vânia Guimarães
Natacha Rosa
Rui Camacho
Rita Ferreira
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Nuno Fonseca
Vítor Santos Costa
Alexandre Magalhães
Miguel de Sousa
Vânia Guimarães
Natacha Rosa
Rui Camacho
Rita Ferreira
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 α-helices and ß-sheets, which are intermediate levels of the local structure.
publishDate 2012
dc.date.none.fl_str_mv 2012-01-01T00:00:00Z
2012
2017-11-16T14:18:44Z
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://repositorio.inesctec.pt/handle/123456789/2925
http://dx.doi.org/10.1504/IJDMB.2012.050265
url http://repositorio.inesctec.pt/handle/123456789/2925
http://dx.doi.org/10.1504/IJDMB.2012.050265
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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