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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. |
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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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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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Registro original: |
Embrapa Gado de Leite (CNPGL) |
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| Registros recuperados : 14 | |
| 3. |  | RIBEIRO, I. M.; BORGES, C. C. H.; ARBEX, W. A.; SILVA, B. Z. da. Programação genética com inicialização baseada em floresta randômica em estudos de associação do genooma completo. In: INTERNATIONAL SODEBRAS CONGRESS, 35., 2016, Foz do Iguaçu. Anais... Publicado na Revista Sodebras, v. 11, n. 129, set. 2016. 6 p.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 4. | | ZONOVELLI, B.; BORGES, C. C. H.; ARBEX, W. A.; OLIVEIRA, F. C. de; RIBEIRO, I. M. Regressão com máquinas de vetores suporte e seleção de atributos via algoritmo genético aplicada em seleção genômica. Revista Sodebras, v. 11, n. 129, set. 2016. Edição do 35º International Sodebras Congress, Foz do Iguaçu, 2016.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 9. | | OLIVEIRA, F. C. DE; ALMEIDA, F. N.; CAPRILES, P. V. S. Z.; ARBEX, W. A.; BORGES, C. C. H. Support vector machine e support vector regression para seleção de SNPs em GWAS. In: SIMPÓSIO DE MECÂNICA COMPUTACIONAL, 11.; ENCONTRO MINEIRO DE MODELAGEM COMPUTACIONAL, 2., 2014, Juiz de Fora. Resumos... Juiz de Fora: [s.n.], 2014. | Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 10. | | MAGALDI, H.; BRAGA, R.; ARBEX, W. A.; CAMPOS, M. M.; BORGES, C. C. H.; DAVID, J. M. N.; CAMPOS, F. Uso de ontologias e técnicas de visualização no apoio às pesquisas em eficiência alimentar de gado de leite. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 11., 2017, Campinas. Ciência de dados na era da agricultura digital: anais. Campinas: Editora da Unicamp: Embrapa Informática Agropecuária, 2017. p. 625-637.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 11. | | MAGALDI, H.; BRAGA, R.; ARBEX, W. A.; CAMPOS, M. M.; BORGES, C. C. H.; DAVID, J. M. N.; CAMPOS, F.; STROELE, V. Análise de Eficiência Alimentar de Gado Leiteiro a partir da Integração de Bases Heterogêneas e Ontologias. In: BRAZILIAN E-SCIENCE WORKSHOP, 38., 2018, Natal-RN. Anais... [S.l.]: Sociedade Brasileira de Computação, 2018.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 12. | | OLIVEIRA, F. C. de; ALMEIDA, F. N.; SILVA, F. F. e; SILVA, M. V. G. B.; BORGES, C. C. H.; ARBEX, W. A. Metodologia para seleção de marcadores com máquina de vetores de suporte com regressão. In: ARBEX, A.; MARTINS, N. F.; MARTINS, M. F. Talking about computing and genomics - TACG vol.1: Modelos e métodos computacionais em Bioinformática. Brasília, DF: Embrapa, 2014. p. 101-126| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 13. | | ARBEX, W. A.; SILVA, F. F. e; SILVA, M. V. G. B.; BORGES, C. C. H.; OLIVEIRA, F. C. de; VARONA, L.; VERNEQUE, R. da S. Decision Support in Attribute Selection with Machine Learning Approach. In: CONFERENCIA IBÉRICA DE SISTEMAS Y TECNOLOGÍAS DE INFORMACION, 9., 2014, Barcelona. Actas... Barcelona: Aisti; Salle, 2014. CISTI 2014| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 14. | | 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. SNPs selection using support vector regression and genetic algorithms in GWAS BMC Genomics, v. 15, article S4, 2014. 15 p. Suppl. 7.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Gado de Leite. |
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| Registros recuperados : 14 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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