文献类型: 外文期刊
作者: Gao; Shang;Yu; Tingxi;Rasheed; Awais;Wang; Jiankang;Crossa; Jose;Hearne; Sarah;Li; Huihui
作者机构:
关键词: artificial intelligence;deep learning;genomic prediction;plant breeding
期刊名称: JOURNAL OF INTEGRATIVE PLANT BIOLOGY
ISSN: 1672-9072
年卷期: 2025 年
页码:
收录情况: SCIE(2025版) ; ; CSCD(2025-2026年度) ; ; 科技核心(2024版)
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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