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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
Bayesian multitrait kernel methods improve multienvironment genome-based prediction
(Oxford University Press, 2022)
Article
Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm
(Genetics Society of America, 2020)
Article
A multivariate Poisson deep learning model for genomic prediction of count data
(Genetics Society of America, 2020)
Article
Maximum a posteriori Threshold Genomic Prediction model for ordinal traits
(Genetics Society of America, 2020)
Article
Origin specific genomic selection: a simple process to optimize the favorable contribution of parents to progeny
(Genetics Society of America, 2020)
Article
A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding
(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 ...