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Items: 35

1.

Joint Use of Genome, Pedigree and Their Interaction with Environment for Predicting the Performance of Wheat Lines in New Environments.

Howard R, Gianola D, Montesinos-López O, Juliana P, Singh R, Poland J, Shrestha S, Pérez-Rodríguez P, Crossa J, Jarquín D.

G3 (Bethesda). 2019 Jul 12. pii: g3.400508.2019. doi: 10.1534/g3.119.400508. [Epub ahead of print]

PMID:
31300481
Free Article
2.

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]

3.

An IoT System for Remote Health Monitoring in Elderly Adults Through a Wearable Device and Mobile Application.

Durán-Vega LA, Santana-Mancilla PC, Buenrostro-Mariscal R, Contreras-Castillo J, Anido-Rifón LE, García-Ruiz MA, Montesinos-López OA, Estrada-González F.

Geriatrics (Basel). 2019 May 7;4(2). pii: E34. doi: 10.3390/geriatrics4020034.

4.

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.

5.

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.

6.

Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat.

Krause MR, González-Pérez L, Crossa J, Pérez-Rodríguez P, Montesinos-López O, Singh RP, Dreisigacker S, Poland J, Rutkoski J, Sorrells M, Gore MA, Mondal S.

G3 (Bethesda). 2019 Apr 9;9(4):1231-1247. doi: 10.1534/g3.118.200856.

7.

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
8.

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.

9.

Prospects and Challenges of Applied Genomic Selection-A New Paradigm in Breeding for Grain Yield in Bread Wheat.

Juliana P, Singh RP, Poland J, Mondal S, Crossa J, Montesinos-López OA, Dreisigacker S, Pérez-Rodríguez P, Huerta-Espino J, Crespo-Herrera L, Govindan V.

Plant Genome. 2018 Nov;11(3). doi: 10.3835/plantgenome2018.03.0017.

10.

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.

11.

Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat.

Juliana P, Montesinos-López OA, Crossa J, Mondal S, González Pérez L, Poland J, Huerta-Espino J, Crespo-Herrera L, Govindan V, Dreisigacker S, Shrestha S, Pérez-Rodríguez P, Pinto Espinosa F, Singh RP.

Theor Appl Genet. 2019 Jan;132(1):177-194. doi: 10.1007/s00122-018-3206-3. Epub 2018 Oct 19.

12.

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.

13.

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.

14.

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.

15.

BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype × Environment Interaction Models.

Granato I, Cuevas J, Luna-Vázquez F, Crossa J, Montesinos-López O, Burgueño J, Fritsche-Neto R.

G3 (Bethesda). 2018 Aug 30;8(9):3039-3047. doi: 10.1534/g3.118.200435.

16.

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.

17.

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.

18.

Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials.

Cuevas J, Granato I, Fritsche-Neto R, Montesinos-Lopez OA, Burgueño J, Bandeira E Sousa M, Crossa J.

G3 (Bethesda). 2018 Mar 28;8(4):1347-1365. doi: 10.1534/g3.117.300454.

19.

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.

20.

Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de Los Campos G, Burgueño J, González-Camacho JM, Pérez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X, Gowda M, Roorkiwal M, Rutkoski J, Varshney RK.

Trends Plant Sci. 2017 Nov;22(11):961-975. doi: 10.1016/j.tplants.2017.08.011. Epub 2017 Sep 28. Review.

PMID:
28965742
21.

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.

22.

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.

23.

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.

24.

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.

25.

Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.

Cuevas J, Crossa J, Soberanis V, Pérez-Elizalde S, Pérez-Rodríguez P, Campos GL, Montesinos-López OA, Burgueño J.

Plant Genome. 2016 Nov;9(3). doi: 10.3835/plantgenome2016.03.0024.

26.

Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.

Cuevas J, Crossa J, Montesinos-López OA, Burgueño J, Pérez-Rodríguez P, de Los Campos G.

G3 (Bethesda). 2017 Jan 5;7(1):41-53. doi: 10.1534/g3.116.035584.

27.

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.

28.

Validation of an instrument to measure tutor performance in promoting self-directed learning by using confirmatory factor analysis.

Amador Fierros G, Montesinos-López OA, Alcaráz Moreno N.

Invest Educ Enferm. 2016 Apr;34(1):74-83. doi: 10.17533/udea.iee.v34n1a09.

29.

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.

30.

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.

31.

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
32.

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.

33.

Integral approach to evaluation of the pathogenic activity of Trypanosoma cruzi clones as exemplified by the Mexican strain.

Melnikov V, Espinoza-Gomez F, Newton-Sanchez O, Delgado-Ensiso I, Montesinos-Lopez OA, Dalin MV, Espinoza B, Martinez I, Sheklakova LA, Dobrovinskaya O, Karpenko LP.

Bull Exp Biol Med. 2013 Nov;156(1):70-2. Erratum in: Bull Exp Biol Med. 2014 Jan;156(3):417.

PMID:
24319733
34.

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.

35.

[Mathematical models for infectious diseases].

Montesinos-López OA, Hernández-Suárez CM.

Salud Publica Mex. 2007 May-Jun;49(3):218-26. Review. Spanish.

PMID:
17589776

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