Bernabe Cano-Páez
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View article: Feature engineering of environmental covariates improves plant genomic-enabled prediction
Feature engineering of environmental covariates improves plant genomic-enabled prediction Open
Introduction Because Genomic selection (GS) is a predictive methodology, it needs to guarantee high-prediction accuracies for practical implementations. However, since many factors affect the prediction performance of this methodology, its…
View article: A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies
A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies Open
While genomic selection (GS) began revolutionizing plant breeding when it was proposed around 20 years ago, its practical implementation is still challenging as many factors affect its accuracy. One such factor is the choice of the statist…
View article: A Comparison between Three Tuning Strategies for Gaussian Kernels in the Context of Univariate Genomic Prediction
A Comparison between Three Tuning Strategies for Gaussian Kernels in the Context of Univariate Genomic Prediction Open
Genomic prediction is revolutionizing plant breeding since candidate genotypes can be selected without the need to measure their trait in the field. When a reference population contains both phenotypic and genotypic information, it is trai…
View article: A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library
A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library Open
Genomic selection (GS) changed the way plant breeders select genotypes. GS takes advantage of phenotypic and genotypic information to training a statistical machine learning model, which is used to predict phenotypic (or breeding) values o…