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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)

Article

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

Montesinos-Lopez, O.A.; Montesinos-Lopez, J.C.; Singh, P.K.; Lozano-Ramirez, N.; Barrón-López, A.; Montesinos-Lopez, A.; Crossa, J. (Genetics Society of America, 2020)

Article

A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction

Montesinos-Lopez, O.A.; Montesinos-López, A.; Crossa, J.; Toledo, F.H.; Montesinos-López, J.C.; Singh, P.K.; Juliana, P.; Salinas Ruiz. J. (Genetics Society of America, 2017)

When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait ...

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)

Article

Genomic prediction enhanced sparse testing for multi-environment trials

Jarquín, D.; Howard, R.; Crossa, J.; Beyene, Y.; Gowda, M.; Martini, J.W.R.; Covarrubias, E.; Burgueño, J.; Pacheco Gil, R. A.; Grondona, M.; Wimmer, V.; Prasanna, B.M. (Genetics Society of America, 2020)

Article

Maximum a posteriori Threshold Genomic Prediction model for ordinal traits

Montesinos-López, A.; Gutierrez-Pulido, H.; Montesinos-Lopez, O.A.; Crossa, J. (Genetics Society of America, 2020)

Article

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

Chin Jian Yang; Sharma, R.; Gorjanc, G.; Hearne, S.; Powell, W.; Mackay, I. (Genetics Society of America, 2020)

Article

A Bayesian decision theory approach for genomic selection

Villar-Hernandez, B.d.J.; Perez-Elizalde, S.; Crossa, J.; Perez-Rodriguez, P.; Toledo, F.H.; Burgueño, J. (Genetics Society of America, 2018)

Plant and animal breeders are interested in selecting the best individuals from a candidate set for the next breeding cycle. In this paper, we propose a formal method under the Bayesian decision theory framework to tackle ...

Article

BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models

Granato, I.; Cuevas, J.; Luna-Vazquez, F.J.; Crossa, J.; Montesinos-Lopez, O.A.; Burgueño, J.; Fritsche-Neto, R. (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

Montesinos-Lopez, O.A.; Montesinos-López, A.; Toledo, F.H.; Pérez-Hernández, O.; Eskridge, K.; Rutkoski, J.; Crossa, J. (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 ...

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

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

A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction

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

Genomic prediction enhanced sparse testing for multi-environment trials

Maximum a posteriori Threshold Genomic Prediction model for ordinal traits

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

A Bayesian decision theory approach for genomic selection

BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models

A genomic bayesian multi-trait and multi-environment model

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

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

A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction

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

Genomic prediction enhanced sparse testing for multi-environment trials

Maximum a posteriori Threshold Genomic Prediction model for ordinal traits

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

A Bayesian decision theory approach for genomic selection

BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models

A genomic bayesian multi-trait and multi-environment model