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Items: 4

1.

A Bayesian Genomic Multi-output Regressor Stacking Model for Predicting Multi-trait Multi-environment Plant Breeding Data.

Montesinos-López O, Montesinos-López A, Crossa J, Cuevas J, Montesinos-López JC, Salas Gutiérrez Z, Lillemo M, Philomin J, Singh R.

G3 (Bethesda). 2019 Aug 19. pii: g3.400336.2019. doi: 10.1534/g3.119.400336. [Epub ahead of print]

2.

Prediction of Multiple-Trait and Multiple-Environment Genomic Data Using Recommender Systems.

Montesinos-López OA, Montesinos-López A, Crossa J, Montesinos-López JC, Mota-Sanchez D, Estrada-González F, Gillberg J, Singh R, Mondal S, Juliana P.

G3 (Bethesda). 2018 Jan 4;8(1):131-147. doi: 10.1534/g3.117.300309.

3.

A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction.

Montesinos-López OA, Montesinos-López A, Crossa J, Montesinos-López JC, Luna-Vázquez FJ, Salinas-Ruiz J, Herrera-Morales JR, Buenrostro-Mariscal R.

G3 (Bethesda). 2017 Jun 7;7(6):1833-1853. doi: 10.1534/g3.117.041202.

4.

A Bayesian Poisson-lognormal Model for Count Data for Multiple-Trait Multiple-Environment Genomic-Enabled Prediction.

Montesinos-López OA, Montesinos-López A, Crossa J, Toledo FH, Montesinos-López JC, Singh P, Juliana P, Salinas-Ruiz J.

G3 (Bethesda). 2017 May 5;7(5):1595-1606. doi: 10.1534/g3.117.039974.

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