Machine learning algorithms translate big data into predictive breeding accuracy
Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability in new lines. Furthermore, ML analyses of genotyp...
| 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/10568/169929 |
| _version_ | 1855526152080195584 |
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| author | Crossa, José Montesinos-Lopez, Osval A. Costa-Neto, Germano Vitale, Paolo Martini, Johannes W.R. Runcie, Daniel E. Fritsche-Neto, Roberto Montesinos-Lopez, Abelardo Perez-Rodriguez, Paulino Gerard, Guillermo S. Dreisigacker, Susanna Crespo-Herrera, Leonardo A. Saint Pierre, Carolina Lillemo, Morten Cuevas, Jaime Bentley, Alison R. Ortiz, Rodomiro |
| author_browse | Bentley, Alison R. Costa-Neto, Germano Crespo-Herrera, Leonardo A. Crossa, José Cuevas, Jaime Dreisigacker, Susanna Fritsche-Neto, Roberto Gerard, Guillermo S. Lillemo, Morten Martini, Johannes W.R. Montesinos-Lopez, Abelardo Montesinos-Lopez, Osval A. Ortiz, Rodomiro Perez-Rodriguez, Paulino Runcie, Daniel E. Saint Pierre, Carolina Vitale, Paolo |
| author_facet | Crossa, José Montesinos-Lopez, Osval A. Costa-Neto, Germano Vitale, Paolo Martini, Johannes W.R. Runcie, Daniel E. Fritsche-Neto, Roberto Montesinos-Lopez, Abelardo Perez-Rodriguez, Paulino Gerard, Guillermo S. Dreisigacker, Susanna Crespo-Herrera, Leonardo A. Saint Pierre, Carolina Lillemo, Morten Cuevas, Jaime Bentley, Alison R. Ortiz, Rodomiro |
| author_sort | Crossa, José |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability in new lines. Furthermore, ML analyses of genotype-by-environment (G×E) interactions can offer insights into the genetic factors that affect performance in specific environments. By leveraging historical breeding data, ML streamlines strategies and automates analyses to reveal genomic patterns. In this review we examine the transformative impact of big data, including multi-trait genomics, phenomics, and environmental covariables, on genomic-enabled prediction in plant breeding. We discuss how big data and ML are revolutionizing the field by enhancing prediction accuracy, deepening our understanding of G×E interactions, and optimizing breeding strategies through the analysis of extensive and diverse datasets. |
| format | Journal Article |
| id | CGSpace169929 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1699292025-10-26T12:51:48Z Machine learning algorithms translate big data into predictive breeding accuracy Crossa, José Montesinos-Lopez, Osval A. Costa-Neto, Germano Vitale, Paolo Martini, Johannes W.R. Runcie, Daniel E. Fritsche-Neto, Roberto Montesinos-Lopez, Abelardo Perez-Rodriguez, Paulino Gerard, Guillermo S. Dreisigacker, Susanna Crespo-Herrera, Leonardo A. Saint Pierre, Carolina Lillemo, Morten Cuevas, Jaime Bentley, Alison R. Ortiz, Rodomiro climate change environment genomics breeding programmes statistical methods machine learning big data Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability in new lines. Furthermore, ML analyses of genotype-by-environment (G×E) interactions can offer insights into the genetic factors that affect performance in specific environments. By leveraging historical breeding data, ML streamlines strategies and automates analyses to reveal genomic patterns. In this review we examine the transformative impact of big data, including multi-trait genomics, phenomics, and environmental covariables, on genomic-enabled prediction in plant breeding. We discuss how big data and ML are revolutionizing the field by enhancing prediction accuracy, deepening our understanding of G×E interactions, and optimizing breeding strategies through the analysis of extensive and diverse datasets. 2025-02 2025-01-25T23:46:20Z 2025-01-25T23:46:20Z Journal Article https://hdl.handle.net/10568/169929 en Open Access application/pdf Elsevier Crossa, J., Montesinos-Lopez, O. A., Costa-Neto, G., Vitale, P., Martini, J. W. R., Runcie, D., Fritsche-Neto, R., Montesinos-Lopez, A., Pérez-Rodríguez, P., Gerard, G., Dreisigacker, S., Crespo-Herrera, L., Pierre, C.S., Lillemo, M., Cuevas, J., Bentley, A., & Ortiz, R. (2024). Machine learning algorithms translate big data into predictive breeding accuracy. Trends in Plant Science. https://doi.org/10.1016/j.tplants.2024.09.011 |
| spellingShingle | climate change environment genomics breeding programmes statistical methods machine learning big data Crossa, José Montesinos-Lopez, Osval A. Costa-Neto, Germano Vitale, Paolo Martini, Johannes W.R. Runcie, Daniel E. Fritsche-Neto, Roberto Montesinos-Lopez, Abelardo Perez-Rodriguez, Paulino Gerard, Guillermo S. Dreisigacker, Susanna Crespo-Herrera, Leonardo A. Saint Pierre, Carolina Lillemo, Morten Cuevas, Jaime Bentley, Alison R. Ortiz, Rodomiro Machine learning algorithms translate big data into predictive breeding accuracy |
| title | Machine learning algorithms translate big data into predictive breeding accuracy |
| title_full | Machine learning algorithms translate big data into predictive breeding accuracy |
| title_fullStr | Machine learning algorithms translate big data into predictive breeding accuracy |
| title_full_unstemmed | Machine learning algorithms translate big data into predictive breeding accuracy |
| title_short | Machine learning algorithms translate big data into predictive breeding accuracy |
| title_sort | machine learning algorithms translate big data into predictive breeding accuracy |
| topic | climate change environment genomics breeding programmes statistical methods machine learning big data |
| url | https://hdl.handle.net/10568/169929 |
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