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Background: Microbial pathogens deploy sophisticated mechanisms to evade host immune responses, complicating the development of effective therapeutics. Artificial intelligence (AI) offers innovative tools to analyze complex host-pathogen interactions and enhance immune defense strategies.
Methods: Recent advances in artificial intelligence (AI) have been applied to high-throughput immune-omics data-sets, structural prediction of microbial proteins, and identification of evasion-related genomic signatures. This letter discusses the emerging applications of machine learning models and neural networks in predicting immune evasion strategies and optimizing immune system support.
Results: Pathogens utilize strategies such as antigenic variation, immune suppression, and molecular mimicry to subvert host immunity. AI-driven approaches, including predictive modeling and machine learning, have been in-strumental in identifying novel therapeutic targets and optimizing immune responses.
Conclusions: The integration of AI into immunological research provides a transformative approach to decoding microbial evasion tactics and developing targeted interventions. Sustained interdisciplinary efforts are critical to advancing this frontier in infectious disease management.
DOI: 10.7754/Clin.Lab.2025.250613
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