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A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding 

Montesinos-Lopez, O.A.; Martin-Vallejo, J.; Crossa, J.; Gianola, D.; Hernández Suárez, C.M.; Montesinos-Lopez, A.; Juliana, P.; Singh, R.P. (Genetics Society of America, 2019)
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A multivariate Poisson deep learning model for genomic prediction of count data 

Montesinos-Lopez, O.A.; Montesinos-Lopez, J.C.; Singh, P.K.; Lozano-Ramirez, N.; Barrón-López, A.; Montesinos-Lopez, A.; Crossa, J. (Genetics Society of America, 2020)
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A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction 

Montesinos-Lopez, O.A.; Montesinos-López, A.; Crossa, J.; Toledo, F.H.; Montesinos-López, J.C.; Singh, P.K.; Juliana, P.; Salinas Ruiz. J. (Genetics Society of America, 2017)
When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait ...
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Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm 

Mageto, E.K.; Crossa, J.; Perez-Rodriguez, P.; Dhliwayo, T.; Palacios-Rojas, N.; Lee, M.; Rui Guo; San Vicente, F.M.; Zhang, X.; Hindu, V. (Genetics Society of America, 2020)
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Genomic prediction enhanced sparse testing for multi-environment trials 

Jarquín, D.; Howard, R.; Crossa, J.; Beyene, Y.; Gowda, M.; Martini, J.W.R.; Covarrubias, E.; Burgueño, J.; Pacheco Gil, R. A.; Grondona, M.; Wimmer, V.; Prasanna, B.M. (Genetics Society of America, 2020)
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Maximum a posteriori Threshold Genomic Prediction model for ordinal traits 

Montesinos-López, A.; Gutierrez-Pulido, H.; Montesinos-Lopez, O.A.; Crossa, J. (Genetics Society of America, 2020)
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Origin specific genomic selection: a simple process to optimize the favorable contribution of parents to progeny 

Chin Jian Yang; Sharma, R.; Gorjanc, G.; Hearne, S.; Powell, W.; Mackay, I. (Genetics Society of America, 2020)
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A Bayesian decision theory approach for genomic selection 

Villar-Hernandez, B.d.J.; Perez-Elizalde, S.; Crossa, J.; Perez-Rodriguez, P.; Toledo, F.H.; Burgueño, J. (Genetics Society of America, 2018)
Plant and animal breeders are interested in selecting the best individuals from a candidate set for the next breeding cycle. In this paper, we propose a formal method under the Bayesian decision theory framework to tackle ...
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BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models 

Granato, I.; Cuevas, J.; Luna-Vazquez, F.J.; Crossa, J.; Montesinos-Lopez, O.A.; Burgueño, J.; Fritsche-Neto, R. (Genetics Society of America, 2018)
One of the major issues in plant breeding is the occurrence of genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were ...
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A genomic bayesian multi-trait and multi-environment model 

Montesinos-Lopez, O.A.; Montesinos-López, A.; Toledo, F.H.; Pérez-Hernández, O.; Eskridge, K.; Rutkoski, J.; Crossa, J. (Genetics Society of America, 2016)
When information on multiple genotypes evaluated in multiple environments is recorded, a multi-environment single trait model for assessing genotype · environment interaction (G · E) is usually employed. Comprehensive ...
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Author
Crossa, J. (19)Montesinos-Lopez, Osval Antonio (11)Pérez-Rodríguez, Paulino (8)Burgueño, J. (7)Montesinos-López, A. (7)Cuevas, Jaime (4)Juliana, P. (4)Toledo, Fernando Henrique (4)Beyene, Y. (3)De Los Campos, Gustavo (3)... View More
Date Issued
2020 (5)2019 (3)2018 (5)2017 (3)2016 (2)2015 (2)2013 (1)2012 (1)
Type
Article (21)
Agrovoc
BAYESIAN THEORY (10)GENOMICS (10)GENOTYPE ENVIRONMENT INTERACTION (8)STATISTICAL METHODS (8)DATA ANALYSIS (5)ARTIFICIAL SELECTION (4)CROP FORECASTING (4)MARKER-ASSISTED SELECTION (4)FORECASTING (3)MAIZE (3)... View More
Keywords
GenPred (22)
Shared Data Resources (22)Genomic Selection (17)Genomic Prediction (7)GBLUP (3)Deep Learning (2)Genomic Enabled Prediction Accuracy (2)Multi-Environment (2)Multi-Trait Multi-Environment (2)Support Vector Machine (2)... View More
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Global (1)


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