Boosting the Detection of Transposable Elements Using Machine Learning

Detalhes bibliográficos
Autor(a) principal: Loureiro,T
Data de Publicação: 2013
Outros Autores: Rui Camacho, Vieira,J, Nuno Fonseca
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/123456789/7068
http://dx.doi.org/10.1007/978-3-319-00578-2_12
Resumo: Transposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single one achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and annotation tools and investigate if Machine Learning techniques can be used to improve their overall detection accuracy. The results of an in silico evaluation of TEs detection and annotation tools indicate that their performance can be improved by using machine learning classifiers. © Springer International Publishing Switzerland 2013.
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spelling Boosting the Detection of Transposable Elements Using Machine LearningTransposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single one achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and annotation tools and investigate if Machine Learning techniques can be used to improve their overall detection accuracy. The results of an in silico evaluation of TEs detection and annotation tools indicate that their performance can be improved by using machine learning classifiers. © Springer International Publishing Switzerland 2013.2018-01-19T11:10:40Z2013-01-01T00:00:00Z2013info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/7068http://dx.doi.org/10.1007/978-3-319-00578-2_12engLoureiro,TRui CamachoVieira,JNuno Fonsecainfo: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:RCAAP2023-05-15T10:20:58Zoai:repositorio.inesctec.pt:123456789/7068Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:52.208430Repositório 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 Boosting the Detection of Transposable Elements Using Machine Learning
title Boosting the Detection of Transposable Elements Using Machine Learning
spellingShingle Boosting the Detection of Transposable Elements Using Machine Learning
Loureiro,T
title_short Boosting the Detection of Transposable Elements Using Machine Learning
title_full Boosting the Detection of Transposable Elements Using Machine Learning
title_fullStr Boosting the Detection of Transposable Elements Using Machine Learning
title_full_unstemmed Boosting the Detection of Transposable Elements Using Machine Learning
title_sort Boosting the Detection of Transposable Elements Using Machine Learning
author Loureiro,T
author_facet Loureiro,T
Rui Camacho
Vieira,J
Nuno Fonseca
author_role author
author2 Rui Camacho
Vieira,J
Nuno Fonseca
author2_role author
author
author
dc.contributor.author.fl_str_mv Loureiro,T
Rui Camacho
Vieira,J
Nuno Fonseca
description Transposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single one achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and annotation tools and investigate if Machine Learning techniques can be used to improve their overall detection accuracy. The results of an in silico evaluation of TEs detection and annotation tools indicate that their performance can be improved by using machine learning classifiers. © Springer International Publishing Switzerland 2013.
publishDate 2013
dc.date.none.fl_str_mv 2013-01-01T00:00:00Z
2013
2018-01-19T11:10:40Z
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/7068
http://dx.doi.org/10.1007/978-3-319-00578-2_12
url http://repositorio.inesctec.pt/handle/123456789/7068
http://dx.doi.org/10.1007/978-3-319-00578-2_12
dc.language.iso.fl_str_mv eng
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