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A 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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778253/ https://www.ncbi.nlm.nih.gov/pubmed/36553548 http://dx.doi.org/10.3390/genes13122279 |
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author | Kismiantini, Montesinos-López, Abelardo Cano-Páez, Bernabe Montesinos-López, J. Cricelio Chavira-Flores, Moisés Montesinos-López, Osval A. Crossa, José |
author_facet | Kismiantini, Montesinos-López, Abelardo Cano-Páez, Bernabe Montesinos-López, J. Cricelio Chavira-Flores, Moisés Montesinos-López, Osval A. Crossa, José |
author_sort | Kismiantini, |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9778253 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97782532022-12-23 A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies Kismiantini, Montesinos-López, Abelardo Cano-Páez, Bernabe Montesinos-López, J. Cricelio Chavira-Flores, Moisés Montesinos-López, Osval A. Crossa, José Genes (Basel) Article 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. MDPI 2022-12-03 /pmc/articles/PMC9778253/ /pubmed/36553548 http://dx.doi.org/10.3390/genes13122279 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kismiantini, Montesinos-López, Abelardo Cano-Páez, Bernabe Montesinos-López, J. Cricelio Chavira-Flores, Moisés Montesinos-López, Osval A. Crossa, José A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies |
title | A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies |
title_full | A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies |
title_fullStr | A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies |
title_full_unstemmed | A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies |
title_short | A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies |
title_sort | multi-trait gaussian kernel genomic prediction model under three tunning strategies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778253/ https://www.ncbi.nlm.nih.gov/pubmed/36553548 http://dx.doi.org/10.3390/genes13122279 |
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