2017
DOI: 10.1590/s0100-204x2017000300009
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Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee

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 F 2 population derived from the self-fertilization of the F 1 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 13… Show more

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Cited by 11 publications
(10 citation statements)
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References 25 publications
(52 reference statements)
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“…For dominant markers in repulsion (allele from the susceptible progenitor Catuai amarelo UFV 2148-57) 1 and -1 were also assigned to the presence and absence of the band, respectively. The codominant markers were coded with 0 for heterozygote, -1 for bands from the resistant progenitor and 1 for bands from the susceptible progenitor (Silva et al, 2017). The genotype data quality control used is described in Pestana et al (2015).…”
Section: Genotyping Of Plantsmentioning
confidence: 99%
“…For dominant markers in repulsion (allele from the susceptible progenitor Catuai amarelo UFV 2148-57) 1 and -1 were also assigned to the presence and absence of the band, respectively. The codominant markers were coded with 0 for heterozygote, -1 for bands from the resistant progenitor and 1 for bands from the susceptible progenitor (Silva et al, 2017). The genotype data quality control used is described in Pestana et al (2015).…”
Section: Genotyping Of Plantsmentioning
confidence: 99%
“…In addition, the use of ANNs in the improvement has already demonstrated the great potential of this methodology in obtained GEBV with simulated studies to classification [30,49]; stability and adaptability [50], and even genomic selection studies [13,17].…”
Section: Discussionmentioning
confidence: 99%
“…In addition, this approach does not require any assumptions about the distribution of phenotypic values as the statistical methods do. ANNs have been used successfully in several breeding studies to predict the genetic merit using simulated [15,16] and real data [17][18][19]. Overall, these studies show that the application of ANN in GS presents great potential for capturing complex interactions since the accuracy values and the bias are, respectively, higher and lower compared with those obtained through traditional GS methodologies (for example, G-BLUP).…”
Section: Introductionmentioning
confidence: 90%
“…As Redes Neurais Artificiais (RNA) são capazes de serem utilizadas para predição e classificação de padrões (Silva et al, 2017). O perceptroné uma forma simples de uma rede neural artificial da qual sua principal aplicação se dá nos problemas de classificações de padrões.…”
Section: Introductionunclassified