| |
|
|
| Registros recuperados : 90 | |
| 26. |  | AZEVEDO, C. F. de; MOURA, A. de A. A.; LOBO, R. N. B.; MODESTO, E. C.; MARTINS FILHO, R. Avaliação de fatores não genéticos sobre características de peso em bovinos Nelore e Guzerá no Estado do Rio Grande do Norte. Revista Ciência Agronômica, Fortaleza, v. 36, n. 2, p. 227-236, maio/ago., 2005.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
|    |
| 27. | | SUELA, M. M.; LIMA, L. P.; AZEVEDO, C. F.; RESENDE, M. D. V. de; NASCIMENTO, M.; SILVA, F. F. e. Combined index of genomic prediction methods applied to productivity traits in rice. Ciência Rural, Santa Maria, v. 49, n. 6, e20181008, June 2019. 9 p.| Biblioteca(s): Embrapa Florestas. |
|  |
| 29. | | AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; LOPES, P. S.; GUIMARÃES, S. E. F. Regressão via componentes independentes aplicada à seleção genômica para características de carcaça em suínos. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 48, n. 6, p. 619-626, jun. 2013.| Biblioteca(s): Embrapa Florestas; Embrapa Unidades Centrais. |
| |
| 30. | | TEODORO, P. E.; AZEVEDO, C. F.; FARIAS, F. J. C.; ALVES, R. S.; PEIXOTO, L. de A.; RIBEIRO, L. P.; CARVALHO, L. P. de; BHERING, L. L. Adaptability of cotton (Gossypium hirsutum) genotypes analysed using a Bayesian AMMI model. Crop and Pasture Science, v. 70, n. 7, p. 615-621, 2019.| Biblioteca(s): Embrapa Algodão. |
|  |
| 31. | | TEODORO, P. E.; FARIAS, F. J. C.; CARVALHO, L. P. de; RIBEIRO, L. P.; NASCIMENTO, M.; AZEVEDO, C. F.; CRUZ, C. D.; BHERING, L. L. Adaptability and stability of cotton genotypes regarding fiber yield and quality traits. Crop Science, v. 59, p. 518?524, March?April 2019.| Biblioteca(s): Embrapa Algodão. |
| |
| 32. | | RESENDE, R. T.; RESENDE, M. D. V.; SILVA, F. F.; AZEVEDO, C. F.; TAKAHASHI, E. K.; SILVA JUNIOR, O. B.; GRATTAPAGLIA, D. Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model. Heredity, v. 119, p. 245-255, 2017.| Biblioteca(s): Embrapa Florestas; Embrapa Recursos Genéticos e Biotecnologia. |
| |
| 33. | | PEREIRA, V. C. S.; DIAS, R. V.; AZEVEDO, C. F. e O.; PEREIRA, I. S.; HONDA, L. S.; PINHEIRO, E. F. M.; CAMPOS, D. V. B. de. Agregação do solo sob diferentes usos e cobertura vegetal no bioma Cerrado. In: CONGRESSO LATINO-AMERICANO DE CIÊNCIA DO SOLO, 23.; CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 38., 2023, Florianópolis. Anais [...]. Florianópolis: Epagri, 2023. p. 949. Ref. ID 1339.| Biblioteca(s): Embrapa Solos. |
| |
| 37. | | AZEVEDO, C. F.; SILVA, F. F. e; RIBEIRO, N. B.; SILVA, D. J. H. da; CECON, P. R.; BARILI, L. D.; PINHEIRO, V. R. Classificação multivariada de curvas de progresso da requeima do tomateiro entre acessos do Banco de Germoplasma de Hortaliças da UFV. Ciência Rural, Santa Maria, v. 42, n. 3, p. 414-417, mar. 2012.| Biblioteca(s): Embrapa Hortaliças. |
| |
| 38. | | COSTA, J. A. da; AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F.; RESENDE, M. D. V. de; NASCIMENTO, A. C. C. Determination of optimal number of independent components in yield traits in rice. Scientia Agricola, v. 79, n. 6, p. 1-8, 2022.| Biblioteca(s): Embrapa Café. |
| |
| 39. | | SUELA, M. M.; AZEVEDO, C. F.; NASCIMENTO, A. C. C.; MOMEN, M.; OLIVEIRA, A. C. B. de; CAIXETA, E. T.; MOROTA, G.; NASCIMENTO, M. Genome-wide association study for morphological, physiological, and productive traits in Coffea arabica using structural equation models. Tree Genetics & Genomes, v. 19, n. 3, 2023. 17 p.| Biblioteca(s): Embrapa Café. |
| |
| 40. | | GOIS, I. B.; BORÉM, A.; CRISTOFANI-YALY, M.; RESENDE, M. D. V. de; AZEVEDO, C. F.; BASTIANEL, M.; NOVELLI, V. M.; MACHADO, M. A. Genome wide selection in citrus breeding. Genetics and Molecular Research, v. 15, n. 4, gmr15048863, Oct. 2016.| Biblioteca(s): Embrapa Florestas. |
| |
| Registros recuperados : 90 | |
|
|
|
Registro Completo
|
Biblioteca(s): |
Embrapa Café. |
|
Data corrente: |
21/01/2022 |
|
Data da última atualização: |
21/01/2022 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 2 |
|
Autoria: |
COSTA, J. A. da; AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F. e; RESENDE, M. D. V. de; NASCIMENTO, A. C. C. |
|
Afiliação: |
JAQUICELE APARECIDA DA COSTA, UFV; CAMILA FERREIRA AZEVEDO, UFV; MOYSÉS NASCIMENTO, UFV; FABYANO FONSECA E SILVA, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa; ANA CAROLINA CAMPANA NASCIMENTO, UFV. |
|
Título: |
A comparison of regression methods based on dimensional reduction for genomic prediction. |
|
Ano de publicação: |
2021 |
|
Fonte/Imprenta: |
Genetics and Molecular Research, v. 20, n. 2, p. 1-15, 2021. |
|
DOI: |
https://doi.org/10.4238/gmr18877 |
|
Idioma: |
Inglês |
|
Conteúdo: |
multicollinearity and high dimensionality problems, making it impossible to obtain stable estimates through the traditional method of estimation based on ordinary least squares. To overcome such challenges, dimensionality reduction methods have been proposed, because of their simple theory and easy application. We compared three dimensionality reduction methods: Principal Components Regression (PCR), Partial Least Squares (PLS), and Independent Components Regression (ICR). An important step for dimensionality reduction and prediction is selecting the number of components, as it affects the linear combinations of the explanatory variables. The linear combinations are inserted into the model to predict the response based on a reduced number of parameters. We examined the criteria for the selection of the number of components. The dimensionality reduction methods were applied to genomic and phenotype data. We evaluated 370 accessions of Asian rice, Oryza sativa, which were genotyped for 36,901 SNPs markers considered to predict the genomic values for the number of panicles per plant trait.This data set presented multicollinearity and high dimensionality. The computational time for each method was also recorded. Among the methods, PCR and ICR gave the highest accuracy values, with ICR standing out for presenting estimates of the least biased genomic values. However, ICR required more computational time than the other methodologies. |
|
Thesaurus NAL: |
Genomics; Regression analysis. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/230432/1/A-comparison-of-regression-methods.pdf
|
|
Marc: |
LEADER 02131naa a2200217 a 4500
001 2139234
005 2022-01-21
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.4238/gmr18877$2DOI
100 1 $aCOSTA, J. A. da
245 $aA comparison of regression methods based on dimensional reduction for genomic prediction.$h[electronic resource]
260 $c2021
520 $amulticollinearity and high dimensionality problems, making it impossible to obtain stable estimates through the traditional method of estimation based on ordinary least squares. To overcome such challenges, dimensionality reduction methods have been proposed, because of their simple theory and easy application. We compared three dimensionality reduction methods: Principal Components Regression (PCR), Partial Least Squares (PLS), and Independent Components Regression (ICR). An important step for dimensionality reduction and prediction is selecting the number of components, as it affects the linear combinations of the explanatory variables. The linear combinations are inserted into the model to predict the response based on a reduced number of parameters. We examined the criteria for the selection of the number of components. The dimensionality reduction methods were applied to genomic and phenotype data. We evaluated 370 accessions of Asian rice, Oryza sativa, which were genotyped for 36,901 SNPs markers considered to predict the genomic values for the number of panicles per plant trait.This data set presented multicollinearity and high dimensionality. The computational time for each method was also recorded. Among the methods, PCR and ICR gave the highest accuracy values, with ICR standing out for presenting estimates of the least biased genomic values. However, ICR required more computational time than the other methodologies.
650 $aGenomics
650 $aRegression analysis
700 1 $aAZEVEDO, C. F.
700 1 $aNASCIMENTO, M.
700 1 $aSILVA, F. F. e
700 1 $aRESENDE, M. D. V. de
700 1 $aNASCIMENTO, A. C. C.
773 $tGenetics and Molecular Research$gv. 20, n. 2, p. 1-15, 2021.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Café (CNPCa) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
