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APA citation
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. (2025). Machine learning algorithms translate big data into predictive breeding accuracy. Trends in Plant Science, 30(2), 167-189. https://doi.org/10.1016/j.tplants.2024.09.011
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Abstract
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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.
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Journal
Trends in Plant Science
Journal volume
30
Journal issue
2
Article number
Place of Publication
United Kingdom
Publisher
Elsevier Ltd.
Donor or Funder
CGIAR Trust Fund
Related Datasets
CGIAR
Initiative
Accelerated Breeding
Impact Area
Nutrition, health & food security
Action Area
Genetic Innovation
CGSpace URL
Program or Accelerator
Breeding for Tomorrow