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An R Package for Bayesian analysis of multi-environment and multi-trait multi-environment data for genome-based prediction

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Luna-Vazquez, F.J.; Toledo, F.H.; Perez-Rodriguez, P.; Lillemo, M.; Crossa, J. (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

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 ...

Article

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 ...

Article

Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm

Mageto, E.K.; Crossa, J.; Perez-Rodriguez, P.; Dhliwayo, T.; Palacios-Rojas, N.; Lee, M.; Rui Guo; San Vicente, F.M.; Zhang, X.; Hindu, V. (Genetics Society of America, 2020)

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An R Package for Bayesian analysis of multi-environment and multi-trait multi-environment data for genome-based prediction

New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes

A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding

A zero altered Poisson random forest model for genomic-enabled prediction

Origin specific genomic selection: a simple process to optimize the favorable contribution of parents to progeny

Maximum a posteriori Threshold Genomic Prediction model for ordinal traits

A multivariate Poisson deep learning model for genomic prediction of count data

Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm

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An R Package for Bayesian analysis of multi-environment and multi-trait multi-environment data for genome-based prediction

New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes

A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding

A zero altered Poisson random forest model for genomic-enabled prediction

Origin specific genomic selection: a simple process to optimize the favorable contribution of parents to progeny

Maximum a posteriori Threshold Genomic Prediction model for ordinal traits

A multivariate Poisson deep learning model for genomic prediction of count data

Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm