Fast-forwarding plant breeding with deep learning-based genomic prediction
Material type:
ArticleLanguage: English Publication details: United Kingdom : John Wiley & Sons Australia, 2025.ISSN: - 1672-9072
- 1744-7909 (Online)
| Item type | Current library | Collection | Status | |
|---|---|---|---|---|
| Article | CIMMYT Knowledge Center: John Woolston Library | CIMMYT Staff Publications Collection | Available |
Peer review
Open Access
Deep learning-based genomic prediction (DL-based GP) has shown promising performance compared to traditional GP methods in plant breeding, particularly in handling large, complex multi-omics data sets. However, the effective development and widespread adoption of DL-based GP still face substantial challenges, including the need for large, high-quality data sets, inconsistencies in performance benchmarking, and the integration of environmental factors. Here, we summarize the key obstacles impeding the development of DL-based GP models and propose future developing directions, such as modular approaches, data augmentation, and advanced attention mechanisms.
Text in English
Awais Rasheed : No CIMMYT Affiliation
National Natural Science Foundation of China Chinese Academy of Agricultural Sciences (CAAS) Breeding for Tomorrow
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