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Mostrando ítems 1-10 de 22
Article
Prediction of count phenotypes using high-resolution images and genomic data
(Genetics Society of America, 2021)
Article
A review of deep learning applications for genomic selection
(BioMed Central, 2021)
Article
Maximum a posteriori Threshold Genomic Prediction model for ordinal traits
(Genetics Society of America, 2020)
Article
A multivariate Poisson deep learning model for genomic prediction of count data
(Genetics Society of America, 2020)
Article
A zero altered Poisson random forest model for genomic-enabled prediction
(Genetics Society of America, 2021)
Article
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
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 ...
Article
BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models
(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 ...
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 ...