Bases de datos bibliográficas:

Publicaciones del académico:

• Improving Wheat Grain Yield Genomic Prediction Accuracy Using Historical Data
• Balancing Sensitivity and Specificity Enhances Top and Bottom Ranking in Genomic Prediction of Cultivars
• Evaluating the Effectiveness of Selection Indices and their Genomic Prediction Using Environmental and Historical Rice Data
• GBLUP Outperforms Quantile Mapping and Outlier Detection for Enhanced Genomic Prediction
• Integration of physiological and remote sensing traits for improved genomic prediction of wheat yield

• Enhancing winter wheat prediction with genomics, phenomics and environmental data
• A Penalized Regression Method for Genomic Prediction Reduces Mismatch between Training and Testing Sets
• Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars
• 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
• Statistical Machine Learning Models Enhance Genomic Prediction of Crop Breeding
• Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software
• An extended multiplicative error model of allometry: Incorporating systematic components, non-normal distributions, and piecewise heteroscedasticity
• Feature engineering of environmental covariates improves plant genomic-enabled prediction
• Refining penalized ridge regression: a novel method for optimizing the regularization parameter in genomic prediction
• A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding
• Genotype Performance Estimation in Targeted Production Environments by Using Sparse Genomic Prediction
• A Feature Weighting Approach for Enhancing within Family Genomic Accuracy
• Machine learning algorithms translate big data into predictive breeding accuracy
• A graph model for genomic prediction in the context of a linear mixed model framework
• Data Augmentation Improves Genomic-Enabled Prediction

• 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?
• Sparse multi‐trait genomic prediction under balanced incomplete block design

• 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
• Linear mixed models
• Artificial neural networks and deep learning for genomic prediction of continuous outcomes
• General Elements of Genomic Selection and Statistical Learning
• Preprocessing Tools for Data Preparation
• Support vector machines and support vector regression
• Random forest for genomic prediction