Artificial neural network analysis of genetic diversity in Carica papaya L.
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Crop Breeding and Applied Biotechnology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332011000300004 |
Resumo: | The study of genetic diversity is fundamental in the preliminary selection of accessions with superior characteristics and for a successful use of these genotypes in breeding programs. The purpose of this study was to evaluate, as a strategy for genetic diversity analysis, the bioinformatics approach called artificial neural network. Based on the average of three growing seasons, eight quantitative traits and thirty-seven papaya accessions were evaluated in a randomized complete block design, with two replications. By Anderson's discriminant analysis, 91.90 % of the accessions were correctly classified in the groups previously defined by artificial neural network. It was concluded that the technique of artificial neural network is feasible to classify the accessions. The presence of significant genetic diversity among accessions was observed. |
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Crop Breeding and Applied Biotechnology |
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Artificial neural network analysis of genetic diversity in Carica papaya L.bioinformaticsmultivariate analysisplant breedingThe study of genetic diversity is fundamental in the preliminary selection of accessions with superior characteristics and for a successful use of these genotypes in breeding programs. The purpose of this study was to evaluate, as a strategy for genetic diversity analysis, the bioinformatics approach called artificial neural network. Based on the average of three growing seasons, eight quantitative traits and thirty-seven papaya accessions were evaluated in a randomized complete block design, with two replications. By Anderson's discriminant analysis, 91.90 % of the accessions were correctly classified in the groups previously defined by artificial neural network. It was concluded that the technique of artificial neural network is feasible to classify the accessions. The presence of significant genetic diversity among accessions was observed.Crop Breeding and Applied Biotechnology2011-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332011000300004Crop Breeding and Applied Biotechnology v.11 n.3 2011reponame:Crop Breeding and Applied Biotechnologyinstname:Sociedade Brasileira de Melhoramento de Plantasinstacron:CBAB10.1590/S1984-70332011000300004info:eu-repo/semantics/openAccessBarbosa,Cibelle DegelViana,Alexandre PioQuintal,Silvana Silva RedPereira,Messias Gonzagaeng2011-10-17T00:00:00Zoai:scielo:S1984-70332011000300004Revistahttps://cbab.sbmp.org.br/#ONGhttps://old.scielo.br/oai/scielo-oai.phpcbabjournal@gmail.com||cbab@ufv.br1984-70331518-7853opendoar:2011-10-17T00:00Crop Breeding and Applied Biotechnology - Sociedade Brasileira de Melhoramento de Plantasfalse |
dc.title.none.fl_str_mv |
Artificial neural network analysis of genetic diversity in Carica papaya L. |
title |
Artificial neural network analysis of genetic diversity in Carica papaya L. |
spellingShingle |
Artificial neural network analysis of genetic diversity in Carica papaya L. Barbosa,Cibelle Degel bioinformatics multivariate analysis plant breeding |
title_short |
Artificial neural network analysis of genetic diversity in Carica papaya L. |
title_full |
Artificial neural network analysis of genetic diversity in Carica papaya L. |
title_fullStr |
Artificial neural network analysis of genetic diversity in Carica papaya L. |
title_full_unstemmed |
Artificial neural network analysis of genetic diversity in Carica papaya L. |
title_sort |
Artificial neural network analysis of genetic diversity in Carica papaya L. |
author |
Barbosa,Cibelle Degel |
author_facet |
Barbosa,Cibelle Degel Viana,Alexandre Pio Quintal,Silvana Silva Red Pereira,Messias Gonzaga |
author_role |
author |
author2 |
Viana,Alexandre Pio Quintal,Silvana Silva Red Pereira,Messias Gonzaga |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Barbosa,Cibelle Degel Viana,Alexandre Pio Quintal,Silvana Silva Red Pereira,Messias Gonzaga |
dc.subject.por.fl_str_mv |
bioinformatics multivariate analysis plant breeding |
topic |
bioinformatics multivariate analysis plant breeding |
description |
The study of genetic diversity is fundamental in the preliminary selection of accessions with superior characteristics and for a successful use of these genotypes in breeding programs. The purpose of this study was to evaluate, as a strategy for genetic diversity analysis, the bioinformatics approach called artificial neural network. Based on the average of three growing seasons, eight quantitative traits and thirty-seven papaya accessions were evaluated in a randomized complete block design, with two replications. By Anderson's discriminant analysis, 91.90 % of the accessions were correctly classified in the groups previously defined by artificial neural network. It was concluded that the technique of artificial neural network is feasible to classify the accessions. The presence of significant genetic diversity among accessions was observed. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332011000300004 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332011000300004 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S1984-70332011000300004 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Crop Breeding and Applied Biotechnology |
publisher.none.fl_str_mv |
Crop Breeding and Applied Biotechnology |
dc.source.none.fl_str_mv |
Crop Breeding and Applied Biotechnology v.11 n.3 2011 reponame:Crop Breeding and Applied Biotechnology instname:Sociedade Brasileira de Melhoramento de Plantas instacron:CBAB |
instname_str |
Sociedade Brasileira de Melhoramento de Plantas |
instacron_str |
CBAB |
institution |
CBAB |
reponame_str |
Crop Breeding and Applied Biotechnology |
collection |
Crop Breeding and Applied Biotechnology |
repository.name.fl_str_mv |
Crop Breeding and Applied Biotechnology - Sociedade Brasileira de Melhoramento de Plantas |
repository.mail.fl_str_mv |
cbabjournal@gmail.com||cbab@ufv.br |
_version_ |
1754209185918091264 |