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Registro Completo |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
10/12/2021 |
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Data da última atualização: |
10/12/2021 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
OLIVEIRA, R. G.; CAMPOLINO, M. L.; LANA, U. G. de P.; OLIVEIRA-PAIVA, C. A.; SANTOS, F. C. dos; GOMES, E. A.; SOUSA, S. M. de. |
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Afiliação: |
Universidade Federal de São João del-Rei; Universidade Federal de São João del-Rei; UBIRACI GOMES DE PAULA LANA, CNPMS; CHRISTIANE ABREU DE OLIVEIRA PAIVA, CNPMS; FLAVIA CRISTINA DOS SANTOS, CNPMS; ELIANE APARECIDA GOMES, CNPMS; SYLVIA MORAIS DE SOUSA TINOCO, CNPMS. |
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Título: |
Impacto do inoculante biológico com bactérias solubilizadoras de fosfatato no microbioma da rizosfera de milho em diferentes condições de fertilização. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
In: SIMPÓSIO DE MICROBIOLOGIA DA UFMG, 8., 2021, Belo Horizonte. Conecta SIM 2021: caderno de resumos. Belo Horizonte: Universidade Federal de Minas Gerais, 2021. |
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Páginas: |
p. 29. |
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Idioma: |
Português |
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Palavras-Chave: |
Bioinoculante; BiomaPhos®; T-RFLP. |
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Categoria do assunto: |
S Ciências Biológicas |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/228958/1/Impacto-inoculante.pdf
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Marc: |
LEADER 00834nam a2200217 a 4500
001 2137577
005 2021-12-10
008 2021 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA, R. G.
245 $aImpacto do inoculante biológico com bactérias solubilizadoras de fosfatato no microbioma da rizosfera de milho em diferentes condições de fertilização.$h[electronic resource]
260 $aIn: SIMPÓSIO DE MICROBIOLOGIA DA UFMG, 8., 2021, Belo Horizonte. Conecta SIM 2021: caderno de resumos. Belo Horizonte: Universidade Federal de Minas Gerais$c2021
300 $ap. 29.
653 $aBioinoculante
653 $aBiomaPhos®
653 $aT-RFLP
700 1 $aCAMPOLINO, M. L.
700 1 $aLANA, U. G. de P.
700 1 $aOLIVEIRA-PAIVA, C. A.
700 1 $aSANTOS, F. C. dos
700 1 $aGOMES, E. A.
700 1 $aSOUSA, S. M. de
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Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
21/01/2022 |
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Data da última atualização: |
21/01/2022 |
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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: |
COSTA, J. A. da; AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F. e; RESENDE, M. D. V. de; NASCIMENTO, A. C. C. |
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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. |
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Título: |
A comparison of regression methods based on dimensional reduction for genomic prediction. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 20, n. 2, p. 1-15, 2021. |
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DOI: |
https://doi.org/10.4238/gmr18877 |
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Idioma: |
Inglês |
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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. |
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Thesaurus NAL: |
Genomics; Regression analysis. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/230432/1/A-comparison-of-regression-methods.pdf
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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.
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Embrapa Café (CNPCa) |
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