A Comparison between Three Tuning Strategies for Gaussian Kernels in the Context of Univariate Genomic Prediction.

ABSTRACT: 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 trained by a statistical machine learning method that is subsequently used for making predictions of breeding or phenotypic values of candidate genotypes that were only genotyped. Nevertheless, the successful implementation of the genomic selection (GS) methodology depends on many factors. One key factor is the type of statistical machine learning method used since some are unable to capture nonlinear patterns available in the data. While kernel methods are powerful statistical machine learning algorithms that capture complex nonlinear patterns in the data, t

SUBMITTER: Montesinos-Lopez OA 

PROVIDER: S-EPMC9778581 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature