| |
|
|
| Registros recuperados : 2 | |
| Registros recuperados : 2 | |
|
|
|
Registro Completo
|
Biblioteca(s): |
Embrapa Pecuária Sul. |
|
Data corrente: |
18/06/2019 |
|
Data da última atualização: |
16/12/2019 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 1 |
|
Autoria: |
FERREIRA, L. M.; SÁFADI, T.; FERREIRA, J. L. |
|
Afiliação: |
Leila Maria Ferreira, UFLA; Thelma Sáfadi, UFLA; JULIANO LINO FERREIRA, CPPSUL. |
|
Título: |
Wavelet-domain elastic net for clustering on genomes strains. |
|
Ano de publicação: |
2018 |
|
Fonte/Imprenta: |
Genetics and Molecular Biology, v. 41, n. 4, p. 884-892, Oct./Dec. 2018. |
|
Idioma: |
Português |
|
Conteúdo: |
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. |
|
Thesagro: |
Análise Estatística; Bactéria Patogênica; Genética; Genoma; Linhagem; Micobactéria. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/198686/1/GMB-2018-0035-1.pdf
|
|
Marc: |
LEADER 01300naa a2200217 a 4500
001 2109931
005 2019-12-16
008 2018 bl uuuu u00u1 u #d
100 1 $aFERREIRA, L. M.
245 $aWavelet-domain elastic net for clustering on genomes strains.$h[electronic resource]
260 $c2018
520 $aWe 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.
650 $aAnálise Estatística
650 $aBactéria Patogênica
650 $aGenética
650 $aGenoma
650 $aLinhagem
650 $aMicobactéria
700 1 $aSÁFADI, T.
700 1 $aFERREIRA, J. L.
773 $tGenetics and Molecular Biology$gv. 41, n. 4, p. 884-892, Oct./Dec. 2018.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Pecuária Sul (CPPSUL) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Expressão de busca inválida. Verifique!!! |
|
|
