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Article
Multi-environment genomic prediction of plant traits using deep learners with dense architecture
(Genetics Society of America, 2018)
Article
Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits
(Genetics Society of America, 2018)
Article
A multivariate Poisson deep learning model for genomic prediction of count data
(Genetics Society of America, 2020)
Article
Bayesian multitrait kernel methods improve multienvironment genome-based prediction
(Oxford University Press, 2022)
Article
Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials
(Genetics Society of America, 2021)
Article
Maximum a posteriori Threshold Genomic Prediction model for ordinal traits
(Genetics Society of America, 2020)
Article
An R Package for Bayesian analysis of multi-environment and multi-trait multi-environment data for genome-based prediction
(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 ...
Article
New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes
(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 ...
Article
A genomic bayesian multi-trait and multi-environment model
(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 ...
Article
Genomic-enabled prediction Kernel models with random intercepts for multi-environment trials
(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 ...