Kohonen's self-organizing maps for the study of genetic dissimilarity among soybean cultivars and genotypes
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFMG |
Texto Completo: | https://doi.org/10.1590/S1678-3921.pab2022.v57.02722 http://hdl.handle.net/1843/59798 https://orcid.org/0000-0002-6877-0656 https://orcid.org/0000-0001-5196-0851 https://orcid.org/0000-0003-2244-1336 https://orcid.org/0000-0001-7854-8111 https://orcid.org/0000-0001-6238-1644 https://orcid.org/0000-0002-8161-8130 |
Resumo: | The objective of this work was to evaluate the genetic dissimilarity between soybean cultivars and genotypes for the selection of parents, as well as to propose a new method for using Kohonen’s self-organizing maps (SOMs) and to test its efficiency through Anderson’s discriminant analysis. The morphoagronomic descriptors of soybean cultivars and genotypes were evaluated. For data analysis, SOM-type artificial neural networks were used. The proposed method allowed the determination of the best network architecture in a nonsubjective way. Furthermore, at the beginning of training, it was possible to mitigate the randomness effect of the synaptic weights on the formed clusters. Six dissimilar clusters were formed; therefore, there is genetic dissimilarity between soybean cultivars and genotypes. Cultivars C25, C8, and C13 can be combined with C36, C31, C32, and C33 because they show good yield-related attributes and high dissimilarity. The proposed methodology is advantageous in comparison with the use of traditional SOMs, besides being efficient due to clustering consistency according to Anderson’s discriminant analysis. |
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2023-10-20T16:12:47Z2023-10-20T16:12:47Z20225718https://doi.org/10.1590/S1678-3921.pab2022.v57.027221678-3921http://hdl.handle.net/1843/59798https://orcid.org/0000-0002-6877-0656https://orcid.org/0000-0001-5196-0851https://orcid.org/0000-0003-2244-1336https://orcid.org/0000-0001-7854-8111https://orcid.org/0000-0001-6238-1644https://orcid.org/0000-0002-8161-8130https://orcid.org/0000-0002-8161-8130The objective of this work was to evaluate the genetic dissimilarity between soybean cultivars and genotypes for the selection of parents, as well as to propose a new method for using Kohonen’s self-organizing maps (SOMs) and to test its efficiency through Anderson’s discriminant analysis. The morphoagronomic descriptors of soybean cultivars and genotypes were evaluated. For data analysis, SOM-type artificial neural networks were used. The proposed method allowed the determination of the best network architecture in a nonsubjective way. Furthermore, at the beginning of training, it was possible to mitigate the randomness effect of the synaptic weights on the formed clusters. Six dissimilar clusters were formed; therefore, there is genetic dissimilarity between soybean cultivars and genotypes. Cultivars C25, C8, and C13 can be combined with C36, C31, C32, and C33 because they show good yield-related attributes and high dissimilarity. The proposed methodology is advantageous in comparison with the use of traditional SOMs, besides being efficient due to clustering consistency according to Anderson’s discriminant analysis.O objetivo deste trabalho foi avaliar a dissimilaridade genética entre cultivares e genótipos de soja para a seleção de genitores, bem como propor um novo método para a utilização de mapas auto-organizáveis de Kohonen (SOMs) e testar sua eficiência por meio da análise discriminante de Anderson. Foram avaliados os descritores morfoagronômicos de cultivares e genótipos de soja. Para análise dos dados, utilizaram-se redes neurais artificiais do tipo SOM. O método proposto permitiu a determinação da melhor arquitetura de rede de forma não subjetiva. Além disso, no início do treinamento, foi possível mitigar o efeito da aleatoriedade dos pesos sinápticos sobre os grupos formados. Foram formados seis grupos dissimilares; portanto, há dissimilaridade genética entre cultivares e genótipos de soja. As cultivares C25, C8 e C13 podem ser combinadas com as C36, C31, C32 e C33, por apresentarem bons atributos de produtividade e alta dissimilaridade. A metodologia proposta é vantajosa em comparação ao uso de SOMs tradicionais e se mostrou eficiente devido à consistência dos agrupamentos de acordo com a análise discriminante de Anderson.CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas GeraisCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorengUniversidade Federal de Minas GeraisUFMGBrasilICA - INSTITUTO DE CIÊNCIAS AGRÁRIASPesquisa Agropecuária BrasileiraSojaRedes neurais (Computação)Análise multivariadaPlantas -- Melhoramento genéticoKohonen's self-organizing maps for the study of genetic dissimilarity among soybean cultivars and genotypesMapas auto-organizáveis de Kohonen no estudo da dissimilaridade genética entre cultivares e genótipos de sojainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articledoi:10.1590/s1678-3921.pab2022.v57.02722Ludimila Geiciane de SáAlcinei Mistico AzevedoCarlos Juliano Brant AlbuquerqueNermy Ribeiro ValadaresOrlando Gonçalves BritoAna Clara Gonçalves FernandesIgnacio Aspiazúinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/59798/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALKohonen’s self-organizing maps for the study of genetic dissimilarity among soybean cultivars and genotypes.pdfKohonen’s self-organizing maps for the study of genetic dissimilarity among soybean cultivars and genotypes.pdfapplication/pdf731530https://repositorio.ufmg.br/bitstream/1843/59798/2/Kohonen%e2%80%99s%20self-organizing%20maps%20for%20the%20study%20of%20genetic%20dissimilarity%20among%20soybean%20cultivars%20and%20genotypes.pdf9d115235f18f7fd75746063ee6d3e6b9MD521843/597982023-10-23 17:21:44.331oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2023-10-23T20:21:44Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false |
dc.title.pt_BR.fl_str_mv |
Kohonen's self-organizing maps for the study of genetic dissimilarity among soybean cultivars and genotypes |
dc.title.alternative.pt_BR.fl_str_mv |
Mapas auto-organizáveis de Kohonen no estudo da dissimilaridade genética entre cultivares e genótipos de soja |
title |
Kohonen's self-organizing maps for the study of genetic dissimilarity among soybean cultivars and genotypes |
spellingShingle |
Kohonen's self-organizing maps for the study of genetic dissimilarity among soybean cultivars and genotypes Ludimila Geiciane de Sá Soja Redes neurais (Computação) Análise multivariada Plantas -- Melhoramento genético |
title_short |
Kohonen's self-organizing maps for the study of genetic dissimilarity among soybean cultivars and genotypes |
title_full |
Kohonen's self-organizing maps for the study of genetic dissimilarity among soybean cultivars and genotypes |
title_fullStr |
Kohonen's self-organizing maps for the study of genetic dissimilarity among soybean cultivars and genotypes |
title_full_unstemmed |
Kohonen's self-organizing maps for the study of genetic dissimilarity among soybean cultivars and genotypes |
title_sort |
Kohonen's self-organizing maps for the study of genetic dissimilarity among soybean cultivars and genotypes |
author |
Ludimila Geiciane de Sá |
author_facet |
Ludimila Geiciane de Sá Alcinei Mistico Azevedo Carlos Juliano Brant Albuquerque Nermy Ribeiro Valadares Orlando Gonçalves Brito Ana Clara Gonçalves Fernandes Ignacio Aspiazú |
author_role |
author |
author2 |
Alcinei Mistico Azevedo Carlos Juliano Brant Albuquerque Nermy Ribeiro Valadares Orlando Gonçalves Brito Ana Clara Gonçalves Fernandes Ignacio Aspiazú |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Ludimila Geiciane de Sá Alcinei Mistico Azevedo Carlos Juliano Brant Albuquerque Nermy Ribeiro Valadares Orlando Gonçalves Brito Ana Clara Gonçalves Fernandes Ignacio Aspiazú |
dc.subject.other.pt_BR.fl_str_mv |
Soja Redes neurais (Computação) Análise multivariada Plantas -- Melhoramento genético |
topic |
Soja Redes neurais (Computação) Análise multivariada Plantas -- Melhoramento genético |
description |
The objective of this work was to evaluate the genetic dissimilarity between soybean cultivars and genotypes for the selection of parents, as well as to propose a new method for using Kohonen’s self-organizing maps (SOMs) and to test its efficiency through Anderson’s discriminant analysis. The morphoagronomic descriptors of soybean cultivars and genotypes were evaluated. For data analysis, SOM-type artificial neural networks were used. The proposed method allowed the determination of the best network architecture in a nonsubjective way. Furthermore, at the beginning of training, it was possible to mitigate the randomness effect of the synaptic weights on the formed clusters. Six dissimilar clusters were formed; therefore, there is genetic dissimilarity between soybean cultivars and genotypes. Cultivars C25, C8, and C13 can be combined with C36, C31, C32, and C33 because they show good yield-related attributes and high dissimilarity. The proposed methodology is advantageous in comparison with the use of traditional SOMs, besides being efficient due to clustering consistency according to Anderson’s discriminant analysis. |
publishDate |
2022 |
dc.date.issued.fl_str_mv |
2022 |
dc.date.accessioned.fl_str_mv |
2023-10-20T16:12:47Z |
dc.date.available.fl_str_mv |
2023-10-20T16:12:47Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1843/59798 |
dc.identifier.doi.pt_BR.fl_str_mv |
https://doi.org/10.1590/S1678-3921.pab2022.v57.02722 |
dc.identifier.issn.pt_BR.fl_str_mv |
1678-3921 |
dc.identifier.orcid.pt_BR.fl_str_mv |
https://orcid.org/0000-0002-6877-0656 https://orcid.org/0000-0001-5196-0851 https://orcid.org/0000-0003-2244-1336 https://orcid.org/0000-0001-7854-8111 https://orcid.org/0000-0001-6238-1644 https://orcid.org/0000-0002-8161-8130 https://orcid.org/0000-0002-8161-8130 |
url |
https://doi.org/10.1590/S1678-3921.pab2022.v57.02722 http://hdl.handle.net/1843/59798 https://orcid.org/0000-0002-6877-0656 https://orcid.org/0000-0001-5196-0851 https://orcid.org/0000-0003-2244-1336 https://orcid.org/0000-0001-7854-8111 https://orcid.org/0000-0001-6238-1644 https://orcid.org/0000-0002-8161-8130 |
identifier_str_mv |
1678-3921 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.none.fl_str_mv |
Pesquisa Agropecuária Brasileira |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
dc.publisher.initials.fl_str_mv |
UFMG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS |
publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
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reponame:Repositório Institucional da UFMG instname:Universidade Federal de Minas Gerais (UFMG) instacron:UFMG |
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UFMG |
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UFMG |
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Repositório Institucional da UFMG |
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