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<p>Artificial intelligence (AI) has emerged as a transformative tool in plant stress biology by enabling the integration and analysis of complex, high-dimensional multi-omics datasets. AI-driven machine learning (ML) and deep learning (DL) models surpass traditional statistical methods by capturing nonlinear, multilayered interactions across genomic, transcriptomic, proteomic, metabolomic and phenotypic data. These approaches reveal hidden regulatory networks, temporal dynamics and context-dependent molecular regulators that govern plant responses to abiotic stress. AI enhances predictive capabilities by forecasting stress tolerance phenotypes, optimizing genome-editing strategies and improving genomic selection through advanced genotype-phenotype modeling. Despite challenges related to data quality, heterogeneity and model interpretability, AI facilitates a systems-level understanding of plant adaptation mechanisms and accelerates precision breeding for climate-resilient crops. Future advances will depend on integrating mechanistic knowledge with data-driven learning and expanding standardized, high-quality datasets to fully realize AI’s potential in crop improvement.</p>

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AI-assisted integretomics approaches in plant stress research

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Tavisha Singh

摘要

Artificial intelligence (AI) has emerged as a transformative tool in plant stress biology by enabling the integration and analysis of complex, high-dimensional multi-omics datasets. AI-driven machine learning (ML) and deep learning (DL) models surpass traditional statistical methods by capturing nonlinear, multilayered interactions across genomic, transcriptomic, proteomic, metabolomic and phenotypic data. These approaches reveal hidden regulatory networks, temporal dynamics and context-dependent molecular regulators that govern plant responses to abiotic stress. AI enhances predictive capabilities by forecasting stress tolerance phenotypes, optimizing genome-editing strategies and improving genomic selection through advanced genotype-phenotype modeling. Despite challenges related to data quality, heterogeneity and model interpretability, AI facilitates a systems-level understanding of plant adaptation mechanisms and accelerates precision breeding for climate-resilient crops. Future advances will depend on integrating mechanistic knowledge with data-driven learning and expanding standardized, high-quality datasets to fully realize AI’s potential in crop improvement.