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...
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2025
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/173853 |
Similar Items: Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software
- Machine learning algorithms translate big data into predictive breeding accuracy
- EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture
- Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data
- A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding
- Deep learning methods improve genomic prediction of wheat breeding
- Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize