Murmu Sneha, Sinha Dipro, Chaurasia Himanshushekhar, Sharma Soumya, Das Ritwika, Jha Girish Kumar, Archak Sunil
Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), New Delhi, India.
Central Institute for Research on Cotton Technology, Indian Council of Agricultural Research (ICAR), Mumbai, India.
Front Plant Sci. 2024 Mar 5;15:1292054. doi: 10.3389/fpls.2024.1292054. eCollection 2024.
Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.
植物巧妙地部署防御系统以应对各种生物和非生物胁迫。涵盖基因组学、转录组学、蛋白质组学和代谢组学的组学技术,彻底改变了对植物防御机制的探索,揭示了植物对各种胁迫源做出反应时的分子复杂性。然而,组学数据的复杂性和规模需要复杂的分析工具才能获得有意义的见解。本综述深入探讨了人工智能算法,特别是机器学习和深度学习,作为在植物防御研究中解读复杂组学数据的有前景的方法。概述涵盖了关键的组学技术,并解决了当前人工智能辅助组学方法固有的挑战和局限性。此外,还思考了这个动态领域潜在的未来方向。总之,人工智能辅助组学技术提供了一个强大的工具包,有助于深入理解植物防御的分子基础,并为在气候变化和新出现疾病的背景下制定更有效的作物保护策略铺平道路。