Deep learning methods improve genomic prediction of wheat breeding
In the field of plant breeding, various machine learning models have been developed and studied to evaluate the genomic prediction (GP) accuracy of unseen phenotypes. Deep learning has shown promise. However, most studies on deep learning in plant breeding have been limited to small datasets, and on...
| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2024
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| Acceso en línea: | https://hdl.handle.net/10568/159859 |
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| author | Montesinos-Lopez, Abelardo Crespo Herrera, Leonardo A. Dreisigacker, Susanna Gerard, Guillermo S. Vitale, Paolo Saint Pierre, Carolina Velu, Govindan Tarekegn, Zerihun Tadesse Chavira-Flores, Moisés Pérez-Rodríguez, Paulino Ramos-Pulido, Sofía Lillemo, Morten Huihui Li Montesinos-Lopez, Osval . Crossa, José |
| author_browse | Chavira-Flores, Moisés Crespo Herrera, Leonardo A. Crossa, José Dreisigacker, Susanna Gerard, Guillermo S. Huihui Li Lillemo, Morten Montesinos-Lopez, Abelardo Montesinos-Lopez, Osval . Pérez-Rodríguez, Paulino Ramos-Pulido, Sofía Saint Pierre, Carolina Tarekegn, Zerihun Tadesse Velu, Govindan Vitale, Paolo |
| author_facet | Montesinos-Lopez, Abelardo Crespo Herrera, Leonardo A. Dreisigacker, Susanna Gerard, Guillermo S. Vitale, Paolo Saint Pierre, Carolina Velu, Govindan Tarekegn, Zerihun Tadesse Chavira-Flores, Moisés Pérez-Rodríguez, Paulino Ramos-Pulido, Sofía Lillemo, Morten Huihui Li Montesinos-Lopez, Osval . Crossa, José |
| author_sort | Montesinos-Lopez, Abelardo |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | In the field of plant breeding, various machine learning models have been developed and studied to evaluate the genomic prediction (GP) accuracy of unseen phenotypes. Deep learning has shown promise. However, most studies on deep learning in plant breeding have been limited to small datasets, and only a few have explored its application in moderate-sized datasets. In this study, we aimed to address this limitation by utilizing a moderately large dataset. We examined the performance of a deep learning (DL) model and compared it with the widely used and powerful best linear unbiased prediction (GBLUP) model. The goal was to assess the GP accuracy in the context of a five-fold cross-validation strategy and when predicting complete environments using the DL model. The results revealed the DL model outperformed the GBLUP model in terms of GP accuracy for two out of the five included traits in the five-fold cross-validation strategy, with similar results in the other traits. This indicates the superiority of the DL model in predicting these specific traits. Furthermore, when predicting complete environments using the leave-one-environment-out (LOEO) approach, the DL model demonstrated competitive performance. It is worth noting that the DL model employed in this study extends a previously proposed multi-modal DL model, which had been primarily applied to image data but with small datasets. By utilizing a moderately large dataset, we were able to evaluate the performance and potential of the DL model in a context with more information and challenging scenario in plant breeding. |
| format | Journal Article |
| id | CGSpace159859 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1598592025-12-08T10:29:22Z Deep learning methods improve genomic prediction of wheat breeding Montesinos-Lopez, Abelardo Crespo Herrera, Leonardo A. Dreisigacker, Susanna Gerard, Guillermo S. Vitale, Paolo Saint Pierre, Carolina Velu, Govindan Tarekegn, Zerihun Tadesse Chavira-Flores, Moisés Pérez-Rodríguez, Paulino Ramos-Pulido, Sofía Lillemo, Morten Huihui Li Montesinos-Lopez, Osval . Crossa, José genomics forecasting machine learning learning wheat plant breeding In the field of plant breeding, various machine learning models have been developed and studied to evaluate the genomic prediction (GP) accuracy of unseen phenotypes. Deep learning has shown promise. However, most studies on deep learning in plant breeding have been limited to small datasets, and only a few have explored its application in moderate-sized datasets. In this study, we aimed to address this limitation by utilizing a moderately large dataset. We examined the performance of a deep learning (DL) model and compared it with the widely used and powerful best linear unbiased prediction (GBLUP) model. The goal was to assess the GP accuracy in the context of a five-fold cross-validation strategy and when predicting complete environments using the DL model. The results revealed the DL model outperformed the GBLUP model in terms of GP accuracy for two out of the five included traits in the five-fold cross-validation strategy, with similar results in the other traits. This indicates the superiority of the DL model in predicting these specific traits. Furthermore, when predicting complete environments using the leave-one-environment-out (LOEO) approach, the DL model demonstrated competitive performance. It is worth noting that the DL model employed in this study extends a previously proposed multi-modal DL model, which had been primarily applied to image data but with small datasets. By utilizing a moderately large dataset, we were able to evaluate the performance and potential of the DL model in a context with more information and challenging scenario in plant breeding. 2024-03 2024-11-15T19:42:08Z 2024-11-15T19:42:08Z Journal Article https://hdl.handle.net/10568/159859 en Open Access application/pdf Frontiers Media Montesinos-López, A., Crespo-Herrera, L. A., Dreisigacker, S., Gerard, G. S., Vitale, P., Saint Pierre, C., Velu, G., Tarekegn, Z. T., Chavira-Flores, M., Pérez-Rodríguez, P., Ramos-Pulido, S., Lillemo, M., Li, H., Montesinos-López, O. A., & Crossa, J. (2024). Deep learning methods improve genomic prediction of wheat breeding. Frontiers In Plant Science, 15, 1324090. https://doi.org/10.3389/fpls.2024.1324090 |
| spellingShingle | genomics forecasting machine learning learning wheat plant breeding Montesinos-Lopez, Abelardo Crespo Herrera, Leonardo A. Dreisigacker, Susanna Gerard, Guillermo S. Vitale, Paolo Saint Pierre, Carolina Velu, Govindan Tarekegn, Zerihun Tadesse Chavira-Flores, Moisés Pérez-Rodríguez, Paulino Ramos-Pulido, Sofía Lillemo, Morten Huihui Li Montesinos-Lopez, Osval . Crossa, José Deep learning methods improve genomic prediction of wheat breeding |
| title | Deep learning methods improve genomic prediction of wheat breeding |
| title_full | Deep learning methods improve genomic prediction of wheat breeding |
| title_fullStr | Deep learning methods improve genomic prediction of wheat breeding |
| title_full_unstemmed | Deep learning methods improve genomic prediction of wheat breeding |
| title_short | Deep learning methods improve genomic prediction of wheat breeding |
| title_sort | deep learning methods improve genomic prediction of wheat breeding |
| topic | genomics forecasting machine learning learning wheat plant breeding |
| url | https://hdl.handle.net/10568/159859 |
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