Predicting the secondary structure of proteins using Machine Learning algorithms
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , , , , , |
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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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 |
format |
article |
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 |
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 instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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 |
repository.mail.fl_str_mv |
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1799131605502001152 |