植物盐压力研究中的人工智能:从预测模型到多omics集成
Javier Santos Del Río1,2, Alicia Talavera1, Noé Fernández-Pozo1
1Institute for Mediterranean and Subtropical Horticulture 'La Mayora' (UMA-CSIC), 29010 Malaga, Spain.
Journal of experimental botany
|November 14, 2025
概括
人工智能 (AI) 通过分析复杂的多omics数据,正在彻底改变植物盐应激研究. 人工智能工具加速了耐盐作物的发展,提高了农业产量并减少了经济损失.
科学领域:
- 植物生物学 植物生物学
- 生物信息学是一种生物信息学.
- 农业科学 农业科学
背景情况:
- 盐度对全球农业构成重大威胁,造成不可逆转的植物损害和重大经济损失.
- 传统的单体化学分析为植物盐应激适应机制提供了基础的见解.
研究的目的:
- 探索人工智能 (AI) 对理解和减轻植物盐压力的变革性影响.
- 突出AI在高通量表型化,多omics数据集成和作物改进预测建模方面的作用.
主要方法:
- 使用人工智能集成多omics数据集与生理和形态参数.
- 通过卫星成像和超光谱成像来实现人工智能的高通量表型化.
- 使用变压器和大型语言模型 (LLM) 进行序列分析和生物数据中的模式识别.
主要成果:
- 人工智能能够对复杂的植物对盐应激反应进行高级分析,包括预测应激水平和生长.
- 在蛋白质和核酸序列中,LLM促进了新型模式的发现,有助于识别盐应激耐受机制.
- 基于人工智能的预测农业气候模型显示了提高作物产量和降低耐盐品种的育种成本的潜力.
结论:
- 人工智能是植物盐应激研究中新发现的强大催化剂.
- 人工智能驱动的方法正在为开发弹性作物和在盐水环境中确保粮食安全开辟新的途径.
- 人工智能的整合有望在盐度研究和农业生产率方面取得前所未有的进步.
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