Artificial Intelligence in Immunoinformatics: From Multi-Omics to Precision Immunology

Abstract

The immune system is a multilayered, adaptive network whose behavior emerges across molecules, cells, tissues, and time. Contemporary immunology therefore generates high-dimensional, heterogeneous datasets that strain classical analytical assumptions. Artificial intelligence (AI), spanning machine learning and deep learning, is increasingly becoming a core paradigm for extracting structure, prediction, and actionable insight from these data.

This review summarizes how AI is transforming key steps of modern immunology, from sequencing- and repertoire-based analysis to antigen specificity and epitope prioritization for vaccine design; from single-cell and spatial profiling to inference of immune states and cell–cell communication; and from multi-omics integration to prediction and optimization of immunotherapy responses, including immune checkpoint blockade and CAR-based therapies. We compare the strengths and limitations of major model families—convolutional and recurrent networks, graph neural networks, generative models, and transformer-based architectures—highlighting how their inductive biases map to immunological questions.

We also discuss barriers to broad adoption, including data standardization and metadata quality, interpretability and uncertainty calibration, computational costs, and gaps between benchmark performance and clinical generalizability. Finally, we outline a roadmap toward interpretable and uncertainty-aware models, cross-center data sharing and benchmarking, closed-loop “dry–wet” validation with perturbation experiments, and clinically deployable pipelines for personalized immunodiagnosis and therapy.