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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Pecuária Sul. |
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Data corrente: |
09/01/2018 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
FERREIRA, L. M.; SILVA, J. de A.; SANT'ANA, G. C.; CANÇADO, G. M. de A.; BORÉM, A.; FERREIRA, J. L. |
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Afiliação: |
LEILA MARIA FERREIRA, Ufla; JANAÍNA DE ANDRADE SILVA, UFJF; GUSTAVO CÉSAR SANT’ANA, Agronomic Research for Development, France; GERALDO MAGELA DE ALMEIDA CANCADO, CNPTIA; ALUÍZIO BORÉM, UFV; JULIANO LINO FERREIRA, CPPSUL. |
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Título: |
Application of artificial neural networks in the simulation with genetic data. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
International Journal of Engineering Inventions, v. 6, n. 12, p. 43-46, Dec. 2017. |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: The objective of this work was the concept of applying artificial neural networks in the study of genetic data, in order to make the identification of the microsatellite markers for a particular species of plant to be analyzed more efficient. In this study, was used as an experimental model the data generated for 26 grapevine genotypes were divided into the following populations: Vitis vinifera; North American varieties; and intersp ecific hybrid of rootstocks. After the network training was carried out, an error rate of 0.0003460 was obtained, concluding that the network was able to learn according to the type of data used, even when these data are small. |
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Palavras-Chave: |
Grapevine; Network; Redes neurais. |
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Thesagro: |
Genética; Genótipo. |
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Thesaurus Nal: |
Genotype; Neural networks. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
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100 1 $aFERREIRA, L. M.
245 $aApplication of artificial neural networks in the simulation with genetic data.$h[electronic resource]
260 $c2017
520 $aAbstract: The objective of this work was the concept of applying artificial neural networks in the study of genetic data, in order to make the identification of the microsatellite markers for a particular species of plant to be analyzed more efficient. In this study, was used as an experimental model the data generated for 26 grapevine genotypes were divided into the following populations: Vitis vinifera; North American varieties; and intersp ecific hybrid of rootstocks. After the network training was carried out, an error rate of 0.0003460 was obtained, concluding that the network was able to learn according to the type of data used, even when these data are small.
650 $aGenotype
650 $aNeural networks
650 $aGenética
650 $aGenótipo
653 $aGrapevine
653 $aNetwork
653 $aRedes neurais
700 1 $aSILVA, J. de A.
700 1 $aSANT'ANA, G. C.
700 1 $aCANÇADO, G. M. de A.
700 1 $aBORÉM, A.
700 1 $aFERREIRA, J. L.
773 $tInternational Journal of Engineering Inventions$gv. 6, n. 12, p. 43-46, Dec. 2017.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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