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Prediction of multiple-trait and multiple-environment genomic data using recommender systems 

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Crossa, J.; Montesinos-López, J.C.; Mota-Sanchez, D.; Estrada-González, F.; Gillberg, J.; Singh, R.G.; Mondal, S.; Juliana, P. (Genetics Society of America, 2018)
In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. ...
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An R Package for Bayesian analysis of multi-environment and multi-trait multi-environment data for genome-based prediction 

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Luna-Vazquez, F.J.; Toledo, F.H.; Perez-Rodriguez, P.; Lillemo, M.; Crossa, J. (Genetics Society of America, 2019)
Evidence that genomic selection (GS) is a technology that is revolutionizing plant breeding continues to grow. However, it is very well documented that its success strongly depends on statistical models, which are used by ...
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New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes 

Montesinos-Lopez, O.A.; Martin-Vallejo, J.; Crossa, J.; Gianola, D.; Hernández Suárez, C.M.; Montesinos-López, A.; Juliana, P.; Singh, R.P. (Genetics Society of America, 2019)
Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between ...
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Genomic-enabled prediction Kernel models with random intercepts for multi-environment trials 

Cuevas, J.; Granato, I.; Fritsche-Neto, R.; Montesinos-Lopez, O.A.; Burgueño, J.; Bandeira e Sousa, M.; Crossa, J. (Genetics Society of America, 2018)
In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multienvironment environment-specific ...
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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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A bayesian genomic regression model with skew normal random errors 

Pérez-Rodríguez, P.; Acosta-Pech, R.; Perez-Elizalde, S.; Velasco Cruz, C.; Suarez Espinosa, J.; Crossa, J. (Genetics Society of America, 2018)
Genomic selection (GS) has become a tool for selecting candidates in plant and animal breeding programs. In the case of quantitative traits, it is common to assume that the distribution of the response variable can be ...
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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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Genomic-enabled prediction in maize using kernel models with genotype x environment interaction 

Bandeira e Sousa, M.; Cuevas, J.; De Oliveira Couto, E.G.; Pérez-Rodríguez, P.; Jarquin, D.; Fritsche-Neto, R.; Burgueño, J.; Crossa, J. (Genetics Society of America, 2017)
Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: ...
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Bayesian Genomic Prediction with Genotype x Environment Interaction Kernel Models 

Cuevas, J.; Montesinos-López, Osval A.; Burgueño, J.; Pérez-Rodríguez, P.; De los Campos, G. (Genetics Society of America, 2017)
The phenomenon of genotype · environment (G · E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G · E have been ...
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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. (22)Montesinos-Lopez, O.A. (13)Montesinos-López, A. (10)Pérez-Rodríguez, P. (9)Burgueño, J. (7)Juliana, P. (5)Cuevas, J. (4)Montesinos-Lopez, J.C. (4)Singh, R.P. (4)Toledo, F.H. (4)... View More
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2022 (1)2021 (1)2020 (5)2019 (3)2018 (5)2017 (3)2016 (2)2015 (2)2013 (1)2012 (1)
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Article (24)
Agrovoc
GENOMICS (12)BAYESIAN THEORY (11)GENOTYPE ENVIRONMENT INTERACTION (8)STATISTICAL METHODS (8)DATA ANALYSIS (5)FORECASTING (5)ARTIFICIAL SELECTION (4)CROP FORECASTING (4)MARKER-ASSISTED SELECTION (4)PLANT BREEDING (4)... View More
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Shared Data Resources (24)
GenPred (22)Genomic Selection (16)Genomic Prediction (9)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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