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 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
17/11/2011 |
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Data da última atualização: |
11/10/2017 |
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Tipo da produção científica: |
Nota Técnica/Nota Científica |
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Autoria: |
SILVA, F. F.; VARONA, L.; RESENDE, M. D. V. de; BUENO FILHO, J. S. S.; ROSA, G. J. M.; VIANA, J. M. S. |
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Afiliação: |
Fabyano Fonseca Silva, UFV; Luis Varona, Universidad de Zaragoza; MARCOS DEON VILELA DE RESENDE, CNPF; Júlio Sílvio S. Bueno Filho, UFLA; Guilherme J. M. Rosa, University of Wisconsin; José Marcelo Soriano Viana, UFV. |
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Título: |
A note on accuracy of Bayesian LASSO regression in GWS. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
Livestock Science, v. 142, p. 310-314, 2011. |
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DOI: |
10.1016/j.livsci.2011.09.010 |
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Idioma: |
Inglês |
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Notas: |
Short communication. |
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Conteúdo: |
Several genome wide selection (GWS) statistical methods have been proposed in the last years, and among these stands out the Bayesian LASSO (BL), which is a penalized regression method based on the regularization parameter (?) estimates. In general, the posterior mean values for ? are those that minimize the residual sum of squares (RSS) while controlling the L1 norm (absolute values) of the regression coefficients. However, another option is to use fixed values of ?, which is independent of this minimization process. Nevertheless, the most important aim of GWS is to make predictions about genomic breeding values (GBV=u) for individuals that have not been measured directly for the trait, and for this reason the parameter to maximize should be the accuracy (ru; ?u ). Thus, a question can arise as to whether such estimated ? values that minimize RSS are the same as that which maximize ru; ?u . In order to answer this question, this paper aims to provide methodological and computational resources in order to evaluate the influence of BL regularization parameter estimates on the correlation between true and estimated GBV (accuracy) depending on genetic structure of the target trait (few or many QTLs and low or medium heritability). In general, it is possible to report, on average, that GBV prediction is robust in relation to the ? estimation, since the different values for ? lead to similar accuracy values. Moreover, the fixed ? values grid request high computational costs, implying that the random ? method is more attractive, since it is much faster to use just one Gibbs sampler run, while the grid must to use one run for each fixed ? value. MenosSeveral genome wide selection (GWS) statistical methods have been proposed in the last years, and among these stands out the Bayesian LASSO (BL), which is a penalized regression method based on the regularization parameter (?) estimates. In general, the posterior mean values for ? are those that minimize the residual sum of squares (RSS) while controlling the L1 norm (absolute values) of the regression coefficients. However, another option is to use fixed values of ?, which is independent of this minimization process. Nevertheless, the most important aim of GWS is to make predictions about genomic breeding values (GBV=u) for individuals that have not been measured directly for the trait, and for this reason the parameter to maximize should be the accuracy (ru; ?u ). Thus, a question can arise as to whether such estimated ? values that minimize RSS are the same as that which maximize ru; ?u . In order to answer this question, this paper aims to provide methodological and computational resources in order to evaluate the influence of BL regularization parameter estimates on the correlation between true and estimated GBV (accuracy) depending on genetic structure of the target trait (few or many QTLs and low or medium heritability). In general, it is possible to report, on average, that GBV prediction is robust in relation to the ? estimation, since the different values for ? lead to similar accuracy values. Moreover, the fixed ? values grid request high computational costs, impl... Mostrar Tudo |
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Palavras-Chave: |
Genome wide selection; Penalized regression; SNP markers. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02366naa a2200241 a 4500
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005 2017-10-11
008 2011 bl uuuu u00u1 u #d
024 7 $a10.1016/j.livsci.2011.09.010$2DOI
100 1 $aSILVA, F. F.
245 $aA note on accuracy of Bayesian LASSO regression in GWS.$h[electronic resource]
260 $c2011
500 $aShort communication.
520 $aSeveral genome wide selection (GWS) statistical methods have been proposed in the last years, and among these stands out the Bayesian LASSO (BL), which is a penalized regression method based on the regularization parameter (?) estimates. In general, the posterior mean values for ? are those that minimize the residual sum of squares (RSS) while controlling the L1 norm (absolute values) of the regression coefficients. However, another option is to use fixed values of ?, which is independent of this minimization process. Nevertheless, the most important aim of GWS is to make predictions about genomic breeding values (GBV=u) for individuals that have not been measured directly for the trait, and for this reason the parameter to maximize should be the accuracy (ru; ?u ). Thus, a question can arise as to whether such estimated ? values that minimize RSS are the same as that which maximize ru; ?u . In order to answer this question, this paper aims to provide methodological and computational resources in order to evaluate the influence of BL regularization parameter estimates on the correlation between true and estimated GBV (accuracy) depending on genetic structure of the target trait (few or many QTLs and low or medium heritability). In general, it is possible to report, on average, that GBV prediction is robust in relation to the ? estimation, since the different values for ? lead to similar accuracy values. Moreover, the fixed ? values grid request high computational costs, implying that the random ? method is more attractive, since it is much faster to use just one Gibbs sampler run, while the grid must to use one run for each fixed ? value.
653 $aGenome wide selection
653 $aPenalized regression
653 $aSNP markers
700 1 $aVARONA, L.
700 1 $aRESENDE, M. D. V. de
700 1 $aBUENO FILHO, J. S. S.
700 1 $aROSA, G. J. M.
700 1 $aVIANA, J. M. S.
773 $tLivestock Science$gv. 142, p. 310-314, 2011.
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Registro original: |
Embrapa Florestas (CNPF) |
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Registro Completo
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Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
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Data corrente: |
23/04/2019 |
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Data da última atualização: |
06/12/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
MAES, P.; AMARASINGHE, G. K.; AYLLÓN, M. A.; BASLER, C. F.; SINA, B.; BLASDELL, K. R.; BRIESE, T.; BROWN, P. A.; BUKREYEV, A.; BALKEMA-BUSCHMANN, A.; BUCHHOLZ, U. J.; CHANDRAN, K.; CROZIER, I.; SWART, R. de; DIETZGEN, R. G.; DOLNIK, O.; DOMIER, L. L.; DREXLER, J. F.; DÜRRWALD, R.; DUNDON, W. G.; DUPREX, W. P.; DYE, J. M.; EASTON, A. J.; FOOKS, A. R.; FORMENTY, P. B. H.; FOUCHIER, R. A. M.; ASTUA, J. de F.; GHEDIN, E.; GRIFFITHS, A.; HEWSON, R.; HORIE, M.; HURWITZ, J. L.; HYNDMAN, T. H.; JIANG, D.; KOBINGER, G. P.; KONDO, H.; KURATH, G.; KUZMIN, I. V.; LAMB, R. T A.; LEE, B.; LEROY, E. M.; LI, J.; MARZANO, S. L.; MUHLBERGER, E.; NETESOV, S.; NETESOV, S. V.; PALACIOS, G.; PÁLYI, B.; PAWESKA, J. T.; PAYNE, S. L.; RIMA, B. K.; ROTA, P.; RUBBENSTROTH, D.; SIMMONDS, P.; SMITHER, S. J.; SONG, Q.; SONG, T.; SPANN, K.; STENGLEIN, M. D.; STONE, D. M.; TAKADA, A.; TESH, R. T B.; TOMONAGA, K.; TORDO, N.; TOWNER, J. S.; VAN DEN HOOGEN, B.; VASILAKIS, N.; WAHL, V.; WALKER, P. J.; WANG, D.; WANG, L.-F.; WHITFIELD, A. E.; WILLIAMS, J. V.; YE, G.; ZERBINI, F. M.; ZHANG, Y.-Z.; KUHN, J. H. |
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Afiliação: |
PIET MAES; GAYA K. AMARASINGHE; MARÍA A. AYLLÓN; CHRISTOPHER F. BASLER; SINA BAVARI; KIM R. BLASDELL; THOMAS BRIESE; PAUL A. BROWN; ALEXANDER BUKREYEV; ANNE BALKEMA?BUSCHMANN; URSULA J. BUCHHOLZ; KARTIK CHANDRAN; IAN CROZIER; RIK L. DE SWART; RALF G. DIETZGEN; OLGA DOLNIK; LESLIE L. DOMIER; JAN F. DREXLER; RALF DÜRRWALD; WILLIAM G. DUNDON; W. PAUL DUPREX; JOHN M. DYE; ANDREW J. EASTON; ANTHONY R. FOOKS; PIERRE B. H. FORMENTY; RON A. M. FOUCHIER; JULIANA DE FREITAS ASTUA, CNPMF; ELODIE GHEDIN; ANTHONY GRIFFITHS; ROGER HEWSON; MASAYUKI HORIE; JULIA L. HURWITZ; TIMOTHY H. HYNDMAN; DAOHONG JIANG; GARY P. KOBINGER; HIDEKI KONDO; GAEL KURATH; IVAN V. KUZMIN; ROBERT A. LAMB; BENHUR LEE; ERIC M. LEROY; JIANRONG LI; SHIN-YI L. MARZANO; ELKE MUHLBERGER; SERGEY V. NETESOV; SERGEY V. NETESOV; GUSTAVO PALACIOS; BERNADETT PÁLYI; JANUSZ T. PAWESKA; SUSAN L. PAYNE; BERTUS K. RIMA; PAUL ROTA; DENNIS RUBBENSTROTH; PETER SIMMONDS; SOPHIE J. SMITHER; QISHENG SONG; TIMOTHY SONG; KIRSTEN SPANN; MARK D. STENGLEIN; DAVID M. STONE; AYATO TAKADA; ROBERT B. TESH; KEIZO TOMONAGA; NOEL TORDO; JONATHAN S. TOWNER; BERNADETTE VAN DEN HOOGEN; NIKOS VASILAKIS; VICTORIA WAHL; PETER J. WALKER; DAVID WANG; LIN-FA WANG; ANNA E. WHITFIELD; JOHN V. WILLIAMS; GONGYIN YE; F. MURILO ZERBINI; YONG-ZHEN ZHANG; JENS H. KUHN. |
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Título: |
Taxonomy of the order Mononegavirales: second update 2018. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Archives of Virology, p.1-12, 2019. |
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Idioma: |
Inglês |
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Conteúdo: |
In October 2018, the order Mononegavirales was amended by the establishment of three new families and three new genera, abolishment of two genera, and creation of 28 novel species. This article presents the updated taxonomy of the order Mononegavirales as now accepted by the International Committee on Taxonomy of Viruses (ICTV). |
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Thesaurus NAL: |
Mononegavirales. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02920naa a2201045 a 4500
001 2108425
005 2019-12-06
008 2019 bl uuuu u00u1 u #d
100 1 $aMAES, P.
245 $aTaxonomy of the order Mononegavirales$bsecond update 2018.$h[electronic resource]
260 $c2019
520 $aIn October 2018, the order Mononegavirales was amended by the establishment of three new families and three new genera, abolishment of two genera, and creation of 28 novel species. This article presents the updated taxonomy of the order Mononegavirales as now accepted by the International Committee on Taxonomy of Viruses (ICTV).
650 $aMononegavirales
700 1 $aAMARASINGHE, G. K.
700 1 $aAYLLÓN, M. A.
700 1 $aBASLER, C. F.
700 1 $aSINA, B.
700 1 $aBLASDELL, K. R.
700 1 $aBRIESE, T.
700 1 $aBROWN, P. A.
700 1 $aBUKREYEV, A.
700 1 $aBALKEMA-BUSCHMANN, A.
700 1 $aBUCHHOLZ, U. J.
700 1 $aCHANDRAN, K.
700 1 $aCROZIER, I.
700 1 $aSWART, R. de
700 1 $aDIETZGEN, R. G.
700 1 $aDOLNIK, O.
700 1 $aDOMIER, L. L.
700 1 $aDREXLER, J. F.
700 1 $aDÜRRWALD, R.
700 1 $aDUNDON, W. G.
700 1 $aDUPREX, W. P.
700 1 $aDYE, J. M.
700 1 $aEASTON, A. J.
700 1 $aFOOKS, A. R.
700 1 $aFORMENTY, P. B. H.
700 1 $aFOUCHIER, R. A. M.
700 1 $aASTUA, J. de F.
700 1 $aGHEDIN, E.
700 1 $aGRIFFITHS, A.
700 1 $aHEWSON, R.
700 1 $aHORIE, M.
700 1 $aHURWITZ, J. L.
700 1 $aHYNDMAN, T. H.
700 1 $aJIANG, D.
700 1 $aKOBINGER, G. P.
700 1 $aKONDO, H.
700 1 $aKURATH, G.
700 1 $aKUZMIN, I. V.
700 1 $aLAMB, R. T A.
700 1 $aLEE, B.
700 1 $aLEROY, E. M.
700 1 $aLI, J.
700 1 $aMARZANO, S. L.
700 1 $aMUHLBERGER, E.
700 1 $aNETESOV, S.
700 1 $aNETESOV, S. V.
700 1 $aPALACIOS, G.
700 1 $aPÁLYI, B.
700 1 $aPAWESKA, J. T.
700 1 $aPAYNE, S. L.
700 1 $aRIMA, B. K.
700 1 $aROTA, P.
700 1 $aRUBBENSTROTH, D.
700 1 $aSIMMONDS, P.
700 1 $aSMITHER, S. J.
700 1 $aSONG, Q.
700 1 $aSONG, T.
700 1 $aSPANN, K.
700 1 $aSTENGLEIN, M. D.
700 1 $aSTONE, D. M.
700 1 $aTAKADA, A.
700 1 $aTESH, R. T B.
700 1 $aTOMONAGA, K.
700 1 $aTORDO, N.
700 1 $aTOWNER, J. S.
700 1 $aVAN DEN HOOGEN, B.
700 1 $aVASILAKIS, N.
700 1 $aWAHL, V.
700 1 $aWALKER, P. J.
700 1 $aWANG, D.
700 1 $aWANG, L.-F.
700 1 $aWHITFIELD, A. E.
700 1 $aWILLIAMS, J. V.
700 1 $aYE, G.
700 1 $aZERBINI, F. M.
700 1 $aZHANG, Y.-Z.
700 1 $aKUHN, J. H.
773 $tArchives of Virology, p.1-12, 2019.
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Embrapa Mandioca e Fruticultura (CNPMF) |
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