Fast-forwarding plant breeding with deep learning-based genomic prediction
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 c...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
John Wiley & Sons Australia
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/179111 |
| _version_ | 1855536765017784320 |
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| author | Gao, Shang Yu, Tingxi Rasheed, Awais Wang, Jiankang Crossa, Jose Hearne, Sarah Li, Huihui |
| author_browse | Crossa, Jose Gao, Shang Hearne, Sarah Li, Huihui Rasheed, Awais Wang, Jiankang Yu, Tingxi |
| author_facet | Gao, Shang Yu, Tingxi Rasheed, Awais Wang, Jiankang Crossa, Jose Hearne, Sarah Li, Huihui |
| author_sort | Gao, Shang |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace179111 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | John Wiley & Sons Australia |
| publisherStr | John Wiley & Sons Australia |
| record_format | dspace |
| spelling | CGSpace1791112025-12-20T02:14:49Z Fast-forwarding plant breeding with deep learning-based genomic prediction Gao, Shang Yu, Tingxi Rasheed, Awais Wang, Jiankang Crossa, Jose Hearne, Sarah Li, Huihui artificial intelligence learning genomics forecasting plant breeding 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. 2025-07 2025-12-19T22:46:32Z 2025-12-19T22:46:32Z Journal Article https://hdl.handle.net/10568/179111 en Open Access application/pdf John Wiley & Sons Australia Gao, S., Yu, T., Rasheed, A., Wang, J., Crossa, J., Hearne, S., & Li, H. (2025). Fast‐forwarding plant breeding with deep learning‐based genomic prediction. Journal of Integrative Plant Biology, 67(7), 1700-1705. https://doi.org/10.1111/jipb.13914 |
| spellingShingle | artificial intelligence learning genomics forecasting plant breeding Gao, Shang Yu, Tingxi Rasheed, Awais Wang, Jiankang Crossa, Jose Hearne, Sarah Li, Huihui Fast-forwarding plant breeding with deep learning-based genomic prediction |
| title | Fast-forwarding plant breeding with deep learning-based genomic prediction |
| title_full | Fast-forwarding plant breeding with deep learning-based genomic prediction |
| title_fullStr | Fast-forwarding plant breeding with deep learning-based genomic prediction |
| title_full_unstemmed | Fast-forwarding plant breeding with deep learning-based genomic prediction |
| title_short | Fast-forwarding plant breeding with deep learning-based genomic prediction |
| title_sort | fast forwarding plant breeding with deep learning based genomic prediction |
| topic | artificial intelligence learning genomics forecasting plant breeding |
| url | https://hdl.handle.net/10568/179111 |
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