Wavelet-domain elastic net for clustering on genomes strains

Detalhes bibliográficos
Autor(a) principal: Ferreira,Leila Maria
Data de Publicação: 2018
Outros Autores: Sáfadi,Thelma, Ferreira,Juliano Lino
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Genetics and Molecular Biology
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572018000500884
Resumo: Abstract We propose to evaluate genome similarity by combining discrete non-decimated wavelet transform (NDWT) and elastic net. The wavelets represent a signal with levels of detail, that is, hidden components are detected by means of the decomposition of this signal, where each level provides a different characteristic. The main feature of the elastic net is the grouping of correlated variables where the number of predictors is greater than the number of observations. The combination of these two methodologies applied in the clustering analysis of the Mycobacterium tuberculosis genome strains proved very effective, being able to identify clusters at each level of decomposition.
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spelling Wavelet-domain elastic net for clustering on genomes strainsElastic netgenomeGC-contentcluster analysiswavelet transformAbstract We propose to evaluate genome similarity by combining discrete non-decimated wavelet transform (NDWT) and elastic net. The wavelets represent a signal with levels of detail, that is, hidden components are detected by means of the decomposition of this signal, where each level provides a different characteristic. The main feature of the elastic net is the grouping of correlated variables where the number of predictors is greater than the number of observations. The combination of these two methodologies applied in the clustering analysis of the Mycobacterium tuberculosis genome strains proved very effective, being able to identify clusters at each level of decomposition.Sociedade Brasileira de Genética2018-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572018000500884Genetics and Molecular Biology v.41 n.4 2018reponame:Genetics and Molecular Biologyinstname:Sociedade Brasileira de Genética (SBG)instacron:SBG10.1590/1678-4685-gmb-2018-0035info:eu-repo/semantics/openAccessFerreira,Leila MariaSáfadi,ThelmaFerreira,Juliano Linoeng2019-01-14T00:00:00Zoai:scielo:S1415-47572018000500884Revistahttp://www.gmb.org.br/ONGhttps://old.scielo.br/oai/scielo-oai.php||editor@gmb.org.br1678-46851415-4757opendoar:2019-01-14T00:00Genetics and Molecular Biology - Sociedade Brasileira de Genética (SBG)false
dc.title.none.fl_str_mv Wavelet-domain elastic net for clustering on genomes strains
title Wavelet-domain elastic net for clustering on genomes strains
spellingShingle Wavelet-domain elastic net for clustering on genomes strains
Ferreira,Leila Maria
Elastic net
genome
GC-content
cluster analysis
wavelet transform
title_short Wavelet-domain elastic net for clustering on genomes strains
title_full Wavelet-domain elastic net for clustering on genomes strains
title_fullStr Wavelet-domain elastic net for clustering on genomes strains
title_full_unstemmed Wavelet-domain elastic net for clustering on genomes strains
title_sort Wavelet-domain elastic net for clustering on genomes strains
author Ferreira,Leila Maria
author_facet Ferreira,Leila Maria
Sáfadi,Thelma
Ferreira,Juliano Lino
author_role author
author2 Sáfadi,Thelma
Ferreira,Juliano Lino
author2_role author
author
dc.contributor.author.fl_str_mv Ferreira,Leila Maria
Sáfadi,Thelma
Ferreira,Juliano Lino
dc.subject.por.fl_str_mv Elastic net
genome
GC-content
cluster analysis
wavelet transform
topic Elastic net
genome
GC-content
cluster analysis
wavelet transform
description Abstract We propose to evaluate genome similarity by combining discrete non-decimated wavelet transform (NDWT) and elastic net. The wavelets represent a signal with levels of detail, that is, hidden components are detected by means of the decomposition of this signal, where each level provides a different characteristic. The main feature of the elastic net is the grouping of correlated variables where the number of predictors is greater than the number of observations. The combination of these two methodologies applied in the clustering analysis of the Mycobacterium tuberculosis genome strains proved very effective, being able to identify clusters at each level of decomposition.
publishDate 2018
dc.date.none.fl_str_mv 2018-12-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572018000500884
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572018000500884
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1678-4685-gmb-2018-0035
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Genética
publisher.none.fl_str_mv Sociedade Brasileira de Genética
dc.source.none.fl_str_mv Genetics and Molecular Biology v.41 n.4 2018
reponame:Genetics and Molecular Biology
instname:Sociedade Brasileira de Genética (SBG)
instacron:SBG
instname_str Sociedade Brasileira de Genética (SBG)
instacron_str SBG
institution SBG
reponame_str Genetics and Molecular Biology
collection Genetics and Molecular Biology
repository.name.fl_str_mv Genetics and Molecular Biology - Sociedade Brasileira de Genética (SBG)
repository.mail.fl_str_mv ||editor@gmb.org.br
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