Wang Zhenye, Yuan Hao, Yan Jianbing, Liu Jianxiao
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China.
Plant J. 2025 Jan;121(1):e17190. doi: 10.1111/tpj.17190. Epub 2024 Dec 12.
Due to its excellent performance in processing large amounts of data and capturing complex non-linear relationships, deep learning has been widely applied in many fields of plant biology. Here we first review the application of deep learning in analyzing genome sequences to predict gene expression, chromatin interactions, and epigenetic features (open chromatin, transcription factor binding sites, and methylation sites) in plants. Then, current motif mining and functional component design and synthesis based on generative adversarial networks, large models, and attention mechanisms are elaborated in detail. The progress of protein structure and function prediction, genomic prediction, and large model applications based on deep learning is also discussed. Finally, this work provides prospects for the future development of deep learning in plants with regard to multiple omics data, algorithm optimization, large language models, sequence design, and intelligent breeding.
由于深度学习在处理大量数据和捕捉复杂非线性关系方面表现出色，它已在植物生物学的许多领域中得到广泛应用。在此，我们首先综述深度学习在分析基因组序列以预测植物基因表达、染色质相互作用和表观遗传特征（开放染色质、转录因子结合位点和甲基化位点）方面的应用。然后，详细阐述了基于生成对抗网络、大模型和注意力机制的当前基序挖掘以及功能组件设计与合成。还讨论了基于深度学习的蛋白质结构与功能预测、基因组预测和大模型应用的进展。最后，这项工作从多组学数据、算法优化、大语言模型、序列设计和智能育种等方面为深度学习在植物中的未来发展提供了展望。
