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Items: 23

1.

Deep Kernel for Genomic and Near Infrared Predictions in Multi-Environment Breeding Trials.

Cuevas J, Montesinos-López O, Juliana P, Guzmán C, Pérez-Rodríguez P, González-Bucio J, Burgueño J, Montesinos-López A, Crossa J.

G3 (Bethesda). 2019 Jul 9. pii: g3.400493.2019. doi: 10.1534/g3.119.400493. [Epub ahead of print]

PMID:
31289023
Free Article
2.

Examination of the Effects of Curvature in Geometrical Space on Accuracy of Scaling Derived Projections of Plant Biomass Units: Applications to the Assessment of Average Leaf Biomass in Eelgrass Shoots.

Echavarría-Heras H, Leal-Ramírez C, Villa-Diharce E, Montesinos-López A.

Biomed Res Int. 2019 Apr 23;2019:3613679. doi: 10.1155/2019/3613679. eCollection 2019.

3.

New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes.

Montesinos-López OA, Martín-Vallejo J, Crossa J, Gianola D, Hernández-Suárez CM, Montesinos-López A, Juliana P, Singh R.

G3 (Bethesda). 2019 May 7;9(5):1545-1556. doi: 10.1534/g3.119.300585.

4.

An R Package for Bayesian Analysis of Multi-environment and Multi-trait Multi-environment Data for Genome-Based Prediction.

Montesinos-López OA, Montesinos-López A, Luna-Vázquez FJ, Toledo FH, Pérez-Rodríguez P, Lillemo M, Crossa J.

G3 (Bethesda). 2019 May 7;9(5):1355-1369. doi: 10.1534/g3.119.400126.

5.

A robust Bayesian genome-based median regression model.

Montesinos-López A, Montesinos-López OA, Villa-Diharce ER, Gianola D, Crossa J.

Theor Appl Genet. 2019 May;132(5):1587-1606. doi: 10.1007/s00122-019-03303-6. Epub 2019 Feb 12.

PMID:
30747261
6.

A Benchmarking Between Deep Learning, Support Vector Machine and Bayesian Threshold Best Linear Unbiased Prediction for Predicting Ordinal Traits in Plant Breeding.

Montesinos-López OA, Martín-Vallejo J, Crossa J, Gianola D, Hernández-Suárez CM, Montesinos-López A, Juliana P, Singh R.

G3 (Bethesda). 2019 Feb 7;9(2):601-618. doi: 10.1534/g3.118.200998.

7.

An R Package for Multitrait and Multienvironment Data with the Item-Based Collaborative Filtering Algorithm.

Montesinos-López OA, Luna-Vázquez FJ, Montesinos-López A, Juliana P, Singh R, Crossa J.

Plant Genome. 2018 Nov;11(3). doi: 10.3835/plantgenome2018.02.0013.

8.

Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits.

Montesinos-López OA, Montesinos-López A, Crossa J, Gianola D, Hernández-Suárez CM, Martín-Vallejo J.

G3 (Bethesda). 2018 Dec 10;8(12):3829-3840. doi: 10.1534/g3.118.200728.

9.

Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture.

Montesinos-López A, Montesinos-López OA, Gianola D, Crossa J, Hernández-Suárez CM.

G3 (Bethesda). 2018 Dec 10;8(12):3813-3828. doi: 10.1534/g3.118.200740.

10.

A singular value decomposition Bayesian multiple-trait and multiple-environment genomic model.

Montesinos-López OA, Montesinos-López A, Crossa J, Kismiantini, Ramírez-Alcaraz JM, Singh R, Mondal S, Juliana P.

Heredity (Edinb). 2019 Apr;122(4):381-401. doi: 10.1038/s41437-018-0109-7. Epub 2018 Aug 17.

11.

Correction to: Bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture.

Montesinos-López A, Montesinos-López OA, de Los Campos G, Crossa J, Burgueño J, Luna-Vazquez FJ.

Plant Methods. 2018 Jul 9;14:57. doi: 10.1186/s13007-018-0321-8. eCollection 2018.

12.

Bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture.

Montesinos-López A, Montesinos-López OA, de Los Campos G, Crossa J, Burgueño J, Luna-Vazquez FJ.

Plant Methods. 2018 Jun 11;14:46. doi: 10.1186/s13007-018-0314-7. eCollection 2018. Erratum in: Plant Methods. 2018 Jul 9;14:57.

13.

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.

14.

Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper-spectral image data.

Montesinos-López A, Montesinos-López OA, Cuevas J, Mata-López WA, Burgueño J, Mondal S, Huerta J, Singh R, Autrique E, González-Pérez L, Crossa J.

Plant Methods. 2017 Jul 27;13:62. doi: 10.1186/s13007-017-0212-4. eCollection 2017.

15.

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.

16.

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.

17.

Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data.

Montesinos-López OA, Montesinos-López A, Crossa J, de Los Campos G, Alvarado G, Suchismita M, Rutkoski J, González-Pérez L, Burgueño J.

Plant Methods. 2017 Jan 3;13:4. doi: 10.1186/s13007-016-0154-2. eCollection 2017.

18.

A Genomic Bayesian Multi-trait and Multi-environment Model.

Montesinos-López OA, Montesinos-López A, Crossa J, Toledo FH, Pérez-Hernández O, Eskridge KM, Rutkoski J.

G3 (Bethesda). 2016 Sep 8;6(9):2725-44. doi: 10.1534/g3.116.032359.

19.

Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction.

Montesinos-López A, Montesinos-López OA, Crossa J, Burgueño J, Eskridge KM, Falconi-Castillo E, He X, Singh P, Cichy K.

G3 (Bethesda). 2016 May 3;6(5):1165-77. doi: 10.1534/g3.116.028118.

20.

Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.

Montesinos-López OA, Montesinos-López A, Crossa J, Burgueño J, Eskridge K.

G3 (Bethesda). 2015 Aug 18;5(10):2113-26. doi: 10.1534/g3.115.021154.

21.

Inverse sampling regression for pooled data.

Montesinos-López OA, Montesinos-López A, Eskridge K, Crossa J.

Stat Methods Med Res. 2017 Jun;26(3):1093-1109. doi: 10.1177/0962280214568047. Epub 2015 Jan 19.

PMID:
25601742
22.

Threshold models for genome-enabled prediction of ordinal categorical traits in plant breeding.

Montesinos-López OA, Montesinos-López A, Pérez-Rodríguez P, de Los Campos G, Eskridge K, Crossa J.

G3 (Bethesda). 2014 Dec 23;5(2):291-300. doi: 10.1534/g3.114.016188.

23.

Sample size under inverse negative binomial group testing for accuracy in parameter estimation.

Montesinos-López OA, Montesinos-López A, Crossa J, Eskridge K.

PLoS One. 2012;7(3):e32250. doi: 10.1371/journal.pone.0032250. Epub 2012 Mar 22.

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