| Genomic selection in plant breeding: methods, models, and perspectives J Crossa, P Pérez-Rodríguez, J Cuevas, O Montesinos-López, D Jarquín, ... Trends in plant science 22 (11), 961-975, 2017 | 1201 | 2017 |
| ANALYSIS OF CONSTRUCT VALIDITY AND RELIABILITY OF TWO INSTRUMENTS TO EVALUATE PROFESSIONAL ORIENTED ACTIVITIES. WA Mata-López, LG Juárez-Hernández, S Tobón, OA Montesinos-López Revista Española de Orientación y Psicopedagogía (REOP) 32 (2), 2021 | 223 | 2021 |
| Improving grain yield, stress resilience and quality of bread wheat using large-scale genomics P Juliana, J Poland, J Huerta-Espino, S Shrestha, J Crossa, ... Nature genetics 51 (10), 1530-1539, 2019 | 215 | 2019 |
| A review of deep learning applications for genomic selection OA Montesinos-López, A Montesinos-López, P Pérez-Rodríguez, ... BMC genomics 22, 1-23, 2021 | 165 | 2021 |
| Modelos matemáticos para enfermedades infecciosas OA Montesinos-López, CM Hernández-Suárez Salud pública de México 49, 218-226, 2007 | 148 | 2007 |
| Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models J Cuevas, J Crossa, OA Montesinos-López, J Burgueño, ... G3: Genes, Genomes, Genetics 7 (1), 41-53, 2017 | 142 | 2017 |
| Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data OA Montesinos-López, A Montesinos-López, J Crossa, G de Los Campos, ... Plant methods 13, 1-23, 2017 | 141 | 2017 |
| Genomic prediction of genotype× environment interaction kernel regression models J Cuevas, J Crossa, V Soberanis, S Pérez‐Elizalde, P Pérez‐Rodríguez, ... The plant genome 9 (3), plantgenome2016.03.0024, 2016 | 138 | 2016 |
| A genomic Bayesian multi-trait and multi-environment model OA Montesinos-López, A Montesinos-López, J Crossa, FH Toledo, ... G3: Genes, Genomes, Genetics 6 (9), 2725-2744, 2016 | 128 | 2016 |
| Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits OA Montesinos-López, A Montesinos-López, J Crossa, D Gianola, ... G3: Genes, genomes, genetics 8 (12), 3829-3840, 2018 | 125 | 2018 |
| Multivariate statistical machine learning methods for genomic prediction OA Montesinos López, A Montesinos López, J Crossa Springer Nature, 2022 | 122 | 2022 |
| Multi-environment genomic prediction of plant traits using deep learners with dense architecture A Montesinos-López, OA Montesinos-López, D Gianola, J Crossa, ... G3: Genes, Genomes, Genetics 8 (12), 3813-3828, 2018 | 121 | 2018 |
| The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data J Crossa, R Fritsche-Neto, OA Montesinos-Lopez, G Costa-Neto, ... Frontiers in plant science 12, 651480, 2021 | 110 | 2021 |
| A benchmarking between deep learning, support vector machine and Bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding OA Montesinos-López, J Martín-Vallejo, J Crossa, D Gianola, ... G3: Genes, Genomes, Genetics 9 (2), 601-618, 2019 | 107 | 2019 |
| Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat MR Krause, L González-Pérez, J Crossa, P Pérez-Rodríguez, ... G3: Genes, Genomes, Genetics 9 (4), 1231-1247, 2019 | 105 | 2019 |
| An IoT system for remote health monitoring in elderly adults through a wearable device and mobile application LA Durán-Vega, PC Santana-Mancilla, R Buenrostro-Mariscal, ... Geriatrics 4 (2), 34, 2019 | 101 | 2019 |
| Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat P Juliana, OA Montesinos-López, J Crossa, S Mondal, L González Pérez, ... Theoretical and Applied Genetics 132, 177-194, 2019 | 97 | 2019 |
| Fundamentals of artificial neural networks and deep learning OA Montesinos López, A Montesinos López, J Crossa Multivariate statistical machine learning methods for genomic prediction …, 2022 | 93 | 2022 |
| Prospects and challenges of applied genomic selection—a new paradigm in breeding for grain yield in bread wheat P Juliana, RP Singh, J Poland, S Mondal, J Crossa, ... The plant genome 11 (3), 180017, 2018 | 72 | 2018 |
| New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes OA Montesinos-López, J Martín-Vallejo, J Crossa, D Gianola, ... G3: Genes, genomes, genetics 9 (5), 1545-1556, 2019 | 71 | 2019 |
Jose CrossaBiometricianVerified email at cgiar.org
