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| 1. |  | CORADIN, J.; BRAZ, G. B. P.; SILVA, A. G. da; PROCOPIO, S. de O.; VIAN, G. S.; CHAVAGLIA, P. V. L. L.; GOULART, M. A. R.; SOUZA, M. de F. Selectivity of latifolicides associated with glyphosate applied in postemergence on soybean (Glycine max) cultivars. Revista de la Facultad de Ciencias Agrarias. Universidad Nacional de Cuyo, v. 55, n. 1, p. 86-97, 2023.| Biblioteca(s): Embrapa Meio Ambiente. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
04/12/2014 |
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Data da última atualização: |
05/02/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
OLIVEIRA, F. C. de; BORGES, C. C. H.; ALMEIDA, F. N.; SILVA, F. F. e; VERNEQUE, R. da S.; SILVA, M. V. G. B.; ARBEX, W. A. |
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Afiliação: |
FABRÍZZIO CONDÉ DE OLIVEIRA, UFJF; CARLOS CRISTIANO HASENCLEVER BORGES, UFJF; FERNANDA NASCIMENTO ALMEIDA, FAPEMIG; FABYANO FONSECA E SILVA, UFV; RUI DA SILVA VERNEQUE, CNPGL; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; WAGNER ANTONIO ARBEX, CNPGL. |
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Título: |
SNPs selection using support vector regression and genetic algorithms in GWAS |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
BMC Genomics, v. 15, article S4, 2014. |
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Páginas: |
15 p. |
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Idioma: |
Inglês |
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Notas: |
Suppl. 7. |
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Conteúdo: |
Introduction - This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. Results- The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. Conclusions- The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels. MenosIntroduction - This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. Results- The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. Conclusions- The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction ... Mostrar Tudo |
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Palavras-Chave: |
Single nucleotide polymorphisms (SNPs); SNPs markers; Support Vector Regression with Pearson Universal. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02422naa a2200253 a 4500
001 2001715
005 2024-02-05
008 2014 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA, F. C. de
245 $aSNPs selection using support vector regression and genetic algorithms in GWAS$h[electronic resource]
260 $c2014
300 $a15 p.
500 $aSuppl. 7.
520 $aIntroduction - This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. Results- The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. Conclusions- The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels.
653 $aSingle nucleotide polymorphisms (SNPs)
653 $aSNPs markers
653 $aSupport Vector Regression with Pearson Universal
700 1 $aBORGES, C. C. H.
700 1 $aALMEIDA, F. N.
700 1 $aSILVA, F. F. e
700 1 $aVERNEQUE, R. da S.
700 1 $aSILVA, M. V. G. B.
700 1 $aARBEX, W. A.
773 $tBMC Genomics$gv. 15, article S4, 2014.
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