| Recent advances in biotechnology have catalyzed the rapid emergence of high-throughput omics. This has enabled access to multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and collectively as “panomics”. Panomics represents an integrative framework that unifies multiple layers of ‘omics’ information— generated across diverse individuals and natural genetic variation. Panomics has highlighted in the promoter region of stress tolerance–related genes in plants. By placing artificial intelligence AI at the core of analytical pipelines, panomics transforms high-dimensional raw data into biologically meaningful and experimentally testable hypotheses. Recently integration of image-based phenotyping and advanced computational modeling have enhanced the transformative potential and panomics presents substantial analytical, computational, and interpretative challenges, especially in the integration, standardization, and biological contextualization of heterogeneous datasets. The convergence of panomics and AI has profoundly expanded our understanding of genome plasticity, highlighting the functional importance of dispensable and variable genes. These genes are increasingly recognized for their critical roles in agronomic performance, disease resistance, abiotic stress tolerance, and adaptive phenotypic plasticity. Besides this in recent years artificial intelligence (AI) has demonstrated considerable potential for modeling nonlinear relationships and integrating complementary multimodal information to study biotic and abiotic stress resistance. Collectively, these developments underscore a paradigm shift toward AI-enabled panomics-driven gene discovery. Great progress has been made with AI-based multi-omics analysis and its application in plant stress tolerance will be presented.
Keywords: Panomics, Biotic and Abiotic, Stress tolerance
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