Exploring recent advances, limitations, and future prospects of OMICS-based technologies in plant-pathogen interaction studies: a systematic review

Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the study of plant-pathogen interactions by providing deep insights into the molecular mechanisms that govern plant resistance, susceptibility, and disease progression. These approaches allow for the identification of critical molecular pathways, defense mechanisms, and resistance genes, which serve as valuable targets for disease management and crop breeding. The advent of high-throughput sequencing and artificial intelligence (AI) has further accelerated the analysis of large, complex datasets, enabling predictive models of gene expression, protein interactions, and metabolite dynamics. AI-driven platforms also support the discovery of beneficial microbial communities that enhance plant immunity. Despite these advancements, challenges remain in the integration, standardization, and computational analysis of multi-omics data. Addressing these issues through AI and advanced computational frameworks will be crucial for translating molecular findings into actionable strategies for crop improvement and sustainable pest management. The synergistic application of omics and AI holds transformative potential for advancing agriculture, improving crop resilience, and enhancing global food security. This review systematically covers current advances in genomics, proteomics, transcriptomics, metabolomics, phenomics, and interactomics.