Data/hora: 11/06/2024 17:42:37

Provedor de dados: | 86
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
Fonte: | info:eu-repo/semantics/openAccess