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Article
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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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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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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)
Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this ...
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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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Bayesian multitrait kernel methods improve multienvironment genome-based prediction 

Montesinos-Lopez, O.A.; Montesinos-Lopez, J.C.; Montesinos-Lopez, A.; Ramirez-Alcaraz, J.M.; Poland, J.A.; Singh, R.P.; Dreisigacker, S.; Crespo Herrera, L.A.; Mondal, S.; Velu, G.; Juliana, P.; Huerta-Espino, J.; Shrestha, S.; Varshney, R.K.; Crossa, J. (Oxford University Press, 2022)
Article
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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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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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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)
Article
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A zero altered Poisson random forest model for genomic-enabled prediction 

Montesinos-Lopez, O.A.; Montesinos-López, A.; Mosqueda-Gonzalez, B.A.; Montesinos-Lopez, J.C.; Crossa, J.; Lozano-Ramirez, N.; Singh, P.K.; Valladares-Anguiano, F.A. (Genetics Society of America, 2021)

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Author
Crossa, J. (10)Montesinos-Lopez, O.A. (10)
Montesinos-López, A. (10)
Juliana, P. (5)Montesinos-Lopez, J.C. (5)Singh, P.K. (3)Singh, R.P. (3)Toledo, F.H. (3)Gianola, D. (2)Lozano, N. (2)... View More
Date Issued
2022 (1)2021 (1)2020 (2)2019 (3)2018 (1)2017 (1)2016 (1)
Type
Article (10)
Agrovoc
BAYESIAN THEORY (6)GENOMICS (5)PLANT BREEDING (4)STATISTICAL METHODS (4)MARKER-ASSISTED SELECTION (3)MODELS (3)DATA (2)FORECASTING (2)GENOTYPE ENVIRONMENT INTERACTION (2)ARTIFICIAL SELECTION (1)... View More
Keywords
GenPred (10)
Shared Data Resources (9)Genomic Prediction (7)Genomic Selection (7)Deep Learning (2)Multi-Trait Multi-Environment (2)Support Vector Machine (2)Bayesian Estimation (1)Bayesian Genomic Enabled Prediction (1)Collaborative Foltering (1)... View More


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