Abstract：
The application of deep learning methods, specifically the utilization of Large Language Models (LLMs), in the field of plant biology holds significant promise for generating novel knowledge on plant cell systems. The LLM framework exhibits exceptional potential, particularly with the development of Protein Language Models (PLMs), allowing for in-depth analyses of nucleic acid and protein sequences. This analytical capacity facilitates the discernment of intricate patterns and relationships within biological data, encompassing multi-scale information within DNA or protein sequences. The contribution of PLMs extends beyond mere sequence patterns and structure--function recognition; it also supports advancements in genetic improvements for agriculture. The integration of deep learning approaches into the domain of plant sciences offers opportunities for major breakthroughs in basic research across multi-scale plant traits. Consequently, the strategic application of deep learning methodologies, particularly leveraging the potential of LLMs, will undoubtedly play a pivotal role in advancing plant sciences, plant production, plant uses and propelling the trajectory toward sustainable agroecological and agro-food transitions.
摘要：
深度学习方法的应用,特别是大型语言模型(LLM)的利用，在植物生物学领域，对于产生关于植物细胞系统的新知识具有重要的前景。LLM框架表现出非凡的潜力，特别是随着蛋白质语言模型(PLM)的发展，允许对核酸和蛋白质序列进行深入分析。这种分析能力有助于辨别生物数据中复杂的模式和关系，包含DNA或蛋白质序列中的多尺度信息。PLM的贡献不仅限于序列模式和结构功能识别；它还支持农业遗传改进的进步。将深度学习方法整合到植物科学领域，为跨多尺度植物性状的基础研究提供了重大突破的机会。因此,深度学习方法的战略应用，特别是利用LLM的潜力，无疑将在推进植物科学方面发挥关键作用，工厂生产，植物用途和推动可持续农业生态和农业食品转型的轨迹。
