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对人工智能辅助的omics技术在植物防御方面的审查:当前趋势和未来方向

Sneha Murmu¹, Dipro Sinha¹, Himanshushekhar Chaurasia²

¹Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), New Delhi, India.

Frontiers in plant science

|March 20, 2024

概括

人工智能 (AI) 和omics技术正在彻底改变植物防御研究. 通过人工智能对omics数据的分析,提高了对植物应激反应的理解,以改善作物保护.

科学领域:

植物科学植物科学
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

植物拥有复杂的防御系统,可以抵抗生物和非生物压力.
奥米克技术 (基因组学,转录组学,蛋白质组学,代谢组学) 提供了对植物应激反应的分子见解.
大规模的omics数据需要先进的分析方法.

研究的目的:

审查人工智能 (AI) 算法的应用,特别是机器学习和深度学习,用于分析植物防护omics数据.
探索人工智能如何解读植物防御的基础复杂分子机制.
讨论植物科学中人工智能辅助奥米克的挑战,局限性和未来方向.

主要方法:

对植物防卫研究中的omics技术现有文献的审查.
分析人工智能 (AI) 应用,包括机器学习和深度学习,用于OMIC数据解释.
讨论人工智能驱动的植物防御研究中的挑战和未来前景.

主要成果:

人工智能算法提供了强大的工具,用于分析复杂的,高容量的omics数据在工厂防御.
人工智能有助于更深入地了解涉及植物对压力因素反应的分子通路.
人工智能辅助的omics方法对于开发有效的作物保护策略至关重要.

结论:

人工智能辅助的奥米克技术为了解植物防御机制提供了一个强大的框架.
这种方法对于提高作物抗击气候变化和新出现的疾病的弹性至关重要.
未来的研究应该专注于改进人工智能模型,并整合各种omics数据以获得全面的见解.

关键词:

无生物压力是无生物压力.人工智能的人工智能是人工智能.生物压力是生物压力.深度学习是一种深度学习.机器学习是机器学习.植物植物植物植物植物植物.

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