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Article
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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 ...
Article
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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)

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Author
Crossa, J. (5)
Juliana, P. (5)
Montesinos-Lopez, O.A. (5)Montesinos-López, A. (5)Montesinos-Lopez, J.C. (3)Singh, R.P. (3)Gianola, D. (2)Martin Vallejo, F.J. (2)Mondal, S. (2)Crespo Herrera, Leonardo Abdiel (1)... View More
Date Issued
2022 (1)2019 (2)2018 (1)2017 (1)
Type
Article (5)
Agrovoc
BAYESIAN THEORY (3)GENOMICS (3)PLANT BREEDING (3)STATISTICAL METHODS (3)GENOTYPE ENVIRONMENT INTERACTION (2)ARTIFICIAL SELECTION (1)CROP FORECASTING (1)DATA ANALYSIS (1)FORECASTING (1)MACHINE LEARNING (1)... View More
Keywords
GenPred (5)
Shared Data Resources (5)Genomic Prediction (3)Genomic Selection (3)Deep Learning (2)Bayesian Genomic Enabled Prediction (1)Collaborative Foltering (1)Count Phenotype (1)Genomic Enabled Prediction (1)Genomic Information (1)... View More


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