| Registro completo |
| Provedor de dados: |
PAB
|
| País: |
Brazil
|
| Título: |
Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee
|
| Autores: |
Silva,Gabi Nunes
Nascimento,Moysés
Sant’Anna,Isabela de Castro
Cruz,Cosme Damião
Caixeta,Eveline Teixeira
Carneiro,Pedro Crescêncio Souza
Rosado,Renato Domiciano Silva
Pestana,Kátia Nogueira
Almeida,Dênia Pires de
Oliveira,Marciane da Silva
|
| Data: |
2017-03-01
|
| Ano: |
2017
|
| Palavras-chave: |
Coffea arabica
Hemileia vastatrix
Artificial intelligence
Molecular markers
Prediction
|
| Resumo: |
Abstract: The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F2 population derived from the self-fertilization of the F1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee.
|
| Tipo: |
Info:eu-repo/semantics/article
|
| Idioma: |
Inglês
|
| Identificador: |
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2017000300186
|
| Editor: |
Embrapa Secretaria de Pesquisa e Desenvolvimento
Pesquisa Agropecuária Brasileira
|
| Relação: |
10.1590/s0100-204x2017000300009
|
| Formato: |
text/html
|
| Fonte: |
Pesquisa Agropecuária Brasileira v.52 n.3 2017
|
| Direitos: |
info:eu-repo/semantics/openAccess
|
