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Mostrando ítems 11-20 de 21
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Article
Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper‑spectral image data
(BioMed Central, 2017)
Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ...
Article
Efficacy of plant breeding using genomic information
(John Wiley and Sons Inc, 2023)
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Article
Bayesian multitrait kernel methods improve multienvironment genome-based prediction
(Oxford University Press, 2022)
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Genomic bayesian prediction model for count data with genotype X environment interaction
(Genetics Society of America, 2016)
Genomic tools allow the study of the whole genome and are facilitating the study of genotype-environment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are ...
Article
Prediction of multiple-trait and multiple-environment genomic data using recommender systems
(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. ...
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 ...