Bases de datos bibliográficas:

Publicaciones del académico:

• Data Augmentation Enhances Plant-Genomic-Enabled Predictions
• A marker weighting approach for enhancing within-family accuracy in genomic prediction
• Bayesian discrete lognormal regression model for genomic prediction
• Deep learning methods improve genomic prediction of wheat breeding

• Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments
• Two simple methods to improve the accuracy of the genomic selection methodology
• Designing optimal training sets for genomic prediction using adversarial validation with probit regression
• Multivariate Genomic Hybrid Prediction with Kernels and Parental Information
• Efficacy of plant breeding using genomic information
• Sparse multi-trait genomic prediction under balanced incomplete block design
• Statistical Machine-Learning Methods for Genomic Prediction Using the SKM Library
• Multimodal deep learning methods enhance genomic prediction of wheat breeding
• Optimizing Sparse Testing for Genomic Prediction of Plant Breeding Crops
• Integrating Parental Phenotypic Data Enhances Prediction Accuracy of Hybrids in Wheat Traits
• A novel method for genomic-enabled prediction of cultivars in new environments
• Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?

• A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies
• A Comparison between Three Tuning Strategies for Gaussian Kernels in the Context of Univariate Genomic Prediction
• Multi-trait genome prediction of new environments with partial least squares
• A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library
• Partial Least Squares Enhances Genomic Prediction of New Environments
• A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction
• Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding
• Bayesian multitrait kernel methods improve multienvironment genome-based prediction
• A Revision of the Traditional Analysis Method of Allometry to Allow Extension of the Normality-Borne Complexity of Error Structure: Examining the Adequacy of a Normal-Mixture Distribution-Driven Error Term
• Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding
• Methods in Molecular Biology
• Accounting for Correlation Between Traits in Genomic Prediction
• Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction
• Plant Genotype to Phenotype Prediction Using Machine Learning

• A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits
• A review of deep learning applications for genomic selection
• Application of a Poisson deep neural network model for the prediction of count data in genome-based prediction
• Deep-learning power and perspectives for genomic selection
• Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials
• The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data
• A guide for kernel generalized regression methods for genomic-enabled prediction
• A zero altered Poisson random forest model for genomic-enabled prediction

• A multivariate poisson deep learning model for genomic prediction of count data
• Maximum a posteriori threshold genomic prediction model for ordinal traits
• Genome-based prediction of multiple wheat quality traits in multiple years
• Bayesian regularized quantile regression: A robust alternative for genome-based prediction of skewed data
• Maximum <i>a posteriori</i> Threshold Genomic Prediction Model for Ordinal Traits

• Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning Methods
• Modeling genotype × environment interaction using a factor analytic model of on-farm wheat trials in the Yaqui valley of Mexico
• Multivariate bayesian analysis of on-farm trials with multiple-trait and multiple-environment data
• A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data
• Deep Kernel for genomic and near infrared predictions in multi-environment breeding trials
• An R package for Bayesian analysis of multi-environment and multi-trait multi-environment data for genome-based prediction
• New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes