biostudies-literatureKismiantiniBill & Melinda Gates FoundationSAID projectsCIMMYT CRPBill and Melinda Gates FoundationFoundation for Research Levy on Agricultural Products2279https://www.ebi.ac.uk/biostudies/studies/S-EPMC9778253biostudies-literatureUnknown13(12)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 statistical machine learning method. For this reason, we explore the tuning process under a multi-trait framework using the Gaussian kernel with a multi-trait Bayesian Best Linear Unbiased Predictor (GBLUP) model. We explored three methods of tuning (manual, grid search and Bayesian optimization) using 5 real datasets of breeding programs. We found that using grid search and Bayesian optimization improve between 1.9 and 6.8% the prediction accuracy regarding of using manual tuning. While the improvement in prediction accuracy in some cases can be marginal, it isGenesA Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies.PMC97782533018353200909 MTO 069033INV-003439Chavira-Flores MCrossa JMontesinos-Lopez OAKismiantiniMontesinos-Lopez JCMontesinos-Lopez ACano-Paez BfalseA Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies.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 statistical machine learning method. For this reason, we explore the tuning process under a multi-trait framework using the Gaussian kernel with a multi-trait Bayesian Best Linear Unbiased Predictor (GBLUP) model. We explored three methods of tuning (manual, grid search and Bayesian optimization) using 5 real datasets of breeding programs. We found that using grid search and Bayesian optimization improve between 1.9 and 6.8% the prediction accuracy regarding of using manual tuning. While the improvement in prediction accuracy in some cases can be marginal, it is2022-01-01T00:00:00Z2022 Dec2025-04-18T16:33:32.657Z2025-04-07T03:47:46.589ZS-EPMC97782533655354810.3390/genes13122279