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A Stacking Ensemble Learning Framework for Genomic Prediction
Machine learning (ML) is perhaps the most useful tool for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) is currently unsatisfactory. To improve the genomic predictions, we constructed a stacking ensemble learning...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969712/ https://www.ncbi.nlm.nih.gov/pubmed/33747037 http://dx.doi.org/10.3389/fgene.2021.600040 |
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author | Liang, Mang Chang, Tianpeng An, Bingxing Duan, Xinghai Du, Lili Wang, Xiaoqiao Miao, Jian Xu, Lingyang Gao, Xue Zhang, Lupei Li, Junya Gao, Huijiang |
author_facet | Liang, Mang Chang, Tianpeng An, Bingxing Duan, Xinghai Du, Lili Wang, Xiaoqiao Miao, Jian Xu, Lingyang Gao, Xue Zhang, Lupei Li, Junya Gao, Huijiang |
author_sort | Liang, Mang |
collection | PubMed |
description | Machine learning (ML) is perhaps the most useful tool for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) is currently unsatisfactory. To improve the genomic predictions, we constructed a stacking ensemble learning framework (SELF), integrating three machine learning methods, to predict genomic estimated breeding values (GEBVs). The present study evaluated the prediction ability of SELF by analyzing three real datasets, with different genetic architecture; comparing the prediction accuracy of SELF, base learners, genomic best linear unbiased prediction (GBLUP) and BayesB. For each trait, SELF performed better than base learners, which included support vector regression (SVR), kernel ridge regression (KRR) and elastic net (ENET). The prediction accuracy of SELF was, on average, 7.70% higher than GBLUP in three datasets. Except for the milk fat percentage (MFP) traits, of the German Holstein dairy cattle dataset, SELF was more robust than BayesB in all remaining traits. Therefore, we believed that SEFL has the potential to be promoted to estimate GEBVs in other animals and plants. |
format | Online Article Text |
id | pubmed-7969712 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79697122021-03-19 A Stacking Ensemble Learning Framework for Genomic Prediction Liang, Mang Chang, Tianpeng An, Bingxing Duan, Xinghai Du, Lili Wang, Xiaoqiao Miao, Jian Xu, Lingyang Gao, Xue Zhang, Lupei Li, Junya Gao, Huijiang Front Genet Genetics Machine learning (ML) is perhaps the most useful tool for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) is currently unsatisfactory. To improve the genomic predictions, we constructed a stacking ensemble learning framework (SELF), integrating three machine learning methods, to predict genomic estimated breeding values (GEBVs). The present study evaluated the prediction ability of SELF by analyzing three real datasets, with different genetic architecture; comparing the prediction accuracy of SELF, base learners, genomic best linear unbiased prediction (GBLUP) and BayesB. For each trait, SELF performed better than base learners, which included support vector regression (SVR), kernel ridge regression (KRR) and elastic net (ENET). The prediction accuracy of SELF was, on average, 7.70% higher than GBLUP in three datasets. Except for the milk fat percentage (MFP) traits, of the German Holstein dairy cattle dataset, SELF was more robust than BayesB in all remaining traits. Therefore, we believed that SEFL has the potential to be promoted to estimate GEBVs in other animals and plants. Frontiers Media S.A. 2021-03-04 /pmc/articles/PMC7969712/ /pubmed/33747037 http://dx.doi.org/10.3389/fgene.2021.600040 Text en Copyright © 2021 Liang, Chang, An, Duan, Du, Wang, Miao, Xu, Gao, Zhang, Li and Gao. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Liang, Mang Chang, Tianpeng An, Bingxing Duan, Xinghai Du, Lili Wang, Xiaoqiao Miao, Jian Xu, Lingyang Gao, Xue Zhang, Lupei Li, Junya Gao, Huijiang A Stacking Ensemble Learning Framework for Genomic Prediction |
title | A Stacking Ensemble Learning Framework for Genomic Prediction |
title_full | A Stacking Ensemble Learning Framework for Genomic Prediction |
title_fullStr | A Stacking Ensemble Learning Framework for Genomic Prediction |
title_full_unstemmed | A Stacking Ensemble Learning Framework for Genomic Prediction |
title_short | A Stacking Ensemble Learning Framework for Genomic Prediction |
title_sort | stacking ensemble learning framework for genomic prediction |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969712/ https://www.ncbi.nlm.nih.gov/pubmed/33747037 http://dx.doi.org/10.3389/fgene.2021.600040 |
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