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Fast-forwarding plant breeding with deep learning-based genomic prediction

文献类型： 外文期刊

作者： Gao, Shang 1 ; Yu, Tingxi 1 ; Rasheed, Awais 3 ; Wang, Jiankang 1 ; Crossa, Jose 4 ; Hearne, Sarah 4 ; Li, Huihui 1 ;

作者机构： 1.Chinese Acad Agr Sci CAAS, Inst Crop Sci, State Key Lab Crop Gene Resources & Breeding, CIMMYT China Off, Beijing 100081, Peoples R China

2.Chinese Acad Agr Sci CAAS, Nanfan Res Inst, Sanya 572024, Hainan, Peoples R China

3.Quaid I Azam Univ, Dept Plant Sci, Islamabad 45320, Pakistan

4.Int Maize & Wheat Improvement Ctr CIMMYT, Apdo Postal 6-641, Texcoco 06600, DF, Mexico

关键词： artificial intelligence; deep learning; genomic prediction; plant breeding

期刊名称：JOURNAL OF INTEGRATIVE PLANT BIOLOGY （ 影响因子：9.3； 五年影响因子：10.8 ）

ISSN： 1672-9072

年卷期： 2025 年 67 卷 7 期

页码：

收录情况： SCI

摘要： 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.

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