Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software
With growing evidence that genomic selection (GS) improves genetic gains in plant breeding, it is timely to review the key factors that improve its efficiency. In this feature review, we focus on the statistical machine learning (ML) methods and software that are democratizing GS methodology. We out...
| Autores principales: | , , , , , , , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/173853 |
| _version_ | 1855525731109437440 |
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| author | Crossa, José Martini, Johannes W.R. Vitale, Paolo Perez-Rodriguez, Paulino Costa-Neto, Germano Fritsche-Neto, Roberto Runcie, Daniel E. Cuevas, Jaime Toledo, Fernando H. Huihui Li De Vita, Pasquale Gerard, Guillermo S. Dreisigacker, Susanne Crespo-Herrera, Leonardo A. Saint Pierre, Carolina Bentley, Alison R. Lillemo, Morten Ortiz, Rodomiro Montesinos-Lopez, Osval A. Montesinos-López, Abelardo |
| author_browse | Bentley, Alison R. Costa-Neto, Germano Crespo-Herrera, Leonardo A. Crossa, José Cuevas, Jaime De Vita, Pasquale Dreisigacker, Susanne Fritsche-Neto, Roberto Gerard, Guillermo S. Huihui Li Lillemo, Morten Martini, Johannes W.R. Montesinos-Lopez, Osval A. Montesinos-López, Abelardo Ortiz, Rodomiro Perez-Rodriguez, Paulino Runcie, Daniel E. Saint Pierre, Carolina Toledo, Fernando H. Vitale, Paolo |
| author_facet | Crossa, José Martini, Johannes W.R. Vitale, Paolo Perez-Rodriguez, Paulino Costa-Neto, Germano Fritsche-Neto, Roberto Runcie, Daniel E. Cuevas, Jaime Toledo, Fernando H. Huihui Li De Vita, Pasquale Gerard, Guillermo S. Dreisigacker, Susanne Crespo-Herrera, Leonardo A. Saint Pierre, Carolina Bentley, Alison R. Lillemo, Morten Ortiz, Rodomiro Montesinos-Lopez, Osval A. Montesinos-López, Abelardo |
| author_sort | Crossa, José |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | With growing evidence that genomic selection (GS) improves genetic gains in plant breeding, it is timely to review the key factors that improve its efficiency. In this feature review, we focus on the statistical machine learning (ML) methods and software that are democratizing GS methodology. We outline the principles of genomic-enabled prediction and discuss how statistical ML tools enhance GS efficiency with big data. Additionally, we examine various statistical ML tools developed in recent years for predicting traits across continuous, binary, categorical, and count phenotypes. We highlight the unique advantages of deep learning (DL) models used in genomic prediction (GP). Finally, we review software developed to democratize the use of GP models and recent data management tools that support the adoption of GS methodology. |
| format | Journal Article |
| id | CGSpace173853 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1738532025-10-26T12:55:22Z Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software Crossa, José Martini, Johannes W.R. Vitale, Paolo Perez-Rodriguez, Paulino Costa-Neto, Germano Fritsche-Neto, Roberto Runcie, Daniel E. Cuevas, Jaime Toledo, Fernando H. Huihui Li De Vita, Pasquale Gerard, Guillermo S. Dreisigacker, Susanne Crespo-Herrera, Leonardo A. Saint Pierre, Carolina Bentley, Alison R. Lillemo, Morten Ortiz, Rodomiro Montesinos-Lopez, Osval A. Montesinos-López, Abelardo models plant breeding genomics forecasting software development marker-assisted selection statistical methods machine learning With growing evidence that genomic selection (GS) improves genetic gains in plant breeding, it is timely to review the key factors that improve its efficiency. In this feature review, we focus on the statistical machine learning (ML) methods and software that are democratizing GS methodology. We outline the principles of genomic-enabled prediction and discuss how statistical ML tools enhance GS efficiency with big data. Additionally, we examine various statistical ML tools developed in recent years for predicting traits across continuous, binary, categorical, and count phenotypes. We highlight the unique advantages of deep learning (DL) models used in genomic prediction (GP). Finally, we review software developed to democratize the use of GP models and recent data management tools that support the adoption of GS methodology. 2025-07 2025-03-25T15:01:38Z 2025-03-25T15:01:38Z Journal Article https://hdl.handle.net/10568/173853 en Open Access application/pdf Elsevier Crossa, J., Martini, J. W. R., Vitale, P., Pérez-Rodríguez, P., Costa-Neto, G., Fritsche-Neto, R., Runcie, D., Cuevas, J., Toledo, F., Li, H., De Vita, P., Gerard, G., Dreisigacker, S., Crespo-Herrera, L., Saint Pierre, C., Bentley, A., Lillemo, M., Ortiz, R., Montesinos-López, O. A., & Montesinos-López, A. (2025). Expanding genomic prediction in plant breeding: Harnessing big data, machine learning, and advanced software. Trends in Plant Science, S1360138524003455. https://doi.org/10.1016/j.tplants.2024.12.009 |
| spellingShingle | models plant breeding genomics forecasting software development marker-assisted selection statistical methods machine learning Crossa, José Martini, Johannes W.R. Vitale, Paolo Perez-Rodriguez, Paulino Costa-Neto, Germano Fritsche-Neto, Roberto Runcie, Daniel E. Cuevas, Jaime Toledo, Fernando H. Huihui Li De Vita, Pasquale Gerard, Guillermo S. Dreisigacker, Susanne Crespo-Herrera, Leonardo A. Saint Pierre, Carolina Bentley, Alison R. Lillemo, Morten Ortiz, Rodomiro Montesinos-Lopez, Osval A. Montesinos-López, Abelardo Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software |
| title | Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software |
| title_full | Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software |
| title_fullStr | Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software |
| title_full_unstemmed | Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software |
| title_short | Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software |
| title_sort | expanding genomic prediction in plant breeding harnessing big data machine learning and advanced software |
| topic | models plant breeding genomics forecasting software development marker-assisted selection statistical methods machine learning |
| url | https://hdl.handle.net/10568/173853 |
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