ArticleGenes2022
A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies.
Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Non-linear kernel methods for genomic prediction of soybean yield and quality in multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy.Briefings in bioinformatics · 2026Article
- Bridging the Divide: Integrating Cottonseed Oil Content with Agronomic Trait Improvement in Upland Cotton (Plants (Basel, Switzerland) · 2026Review
- Integrating gene expression data via weighted multiple kernel ridge regression improved accuracy of genomic prediction.Genetics, selection, evolution : GSE · 2025Article
- Enhancing Genomic Prediction Accuracy of Reproduction Traits in Rongchang Pigs Through Machine Learning.Animals : an open access journal from MDPI · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 is very important to carry out the tuning process carefully to improve the accuracy of the GS methodology, even though this entails greater computational resources.
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Registered trials
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