When AI Meets Modern Immunology

When AI Meets Modern Immunology explores how Artificial Intelligence is transforming modern immunology through machine learning, deep learning, computational modeling, and data-driven approaches. It highlights emerging applications in immune regulation, infectious diseases, cancer immunology, autoimmune disorders, vaccine development, diagnostics, drug discovery, and precision immunotherapy. By bridging AI-driven computational methods with experimental and clinical research, the book provides a forward-looking perspective on the future of predictive and personalized immunology.A particular focus is placed on innate immunity, which is increasingly studied through large, heterogeneous, and multimodal datasets. These include bulk and single-cell transcriptomics, chromatin accessibility, spatial omics, proteomics, metabolomics, microbiome profiles, perturbation datasets, and clinical cohorts. Such resources capture host defence, inflammatory regulation, tissue adaptation, and disease progression across complementary biological scales, while also introducing analytical challenges related to sparsity, batch effects, missing modalities, metadata quality, population diversity, and biological interpretation.