Faculty, Staff and Student Publications

Language

English

Publication Date

4-15-2026

Journal

Nature Communications

DOI

10.1038/s41467-026-71364-5

PMID

41986319

PMCID

PMC13260445

PubMedCentral® Posted Date

4-15-2026

PubMedCentral® Full Text Version

Post-print

Abstract

Immunotherapy has seen success in treating patients with cancer, but variable responses underscore the need for effective patient stratification and therapy planning. Computational tools integrating multi-omics, imaging and machine learning have advanced, yet reliable personalized predictions remain challenging. This review analyzes the field through four converging paradigms: classical machine learning, deep learning, graph and network modeling, and mechanistic systems biology. We examine the evolution from correlational features to representation learning, relational inference, and causal simulation of tumor-immune dynamics, highlighting the shift towards multi-modal fusion and interpretable, clinically deployable models. By providing an integrated review of these computational tools, we hope to bring the community closer to achieving precision immuno-oncology for personalized cancer treatments.

Keywords

Humans, Computer Simulation, Immunoinformatics, Immunotherapy, Machine Learning, Neoplasms, Precision Medicine, Soft Computing, Systems Biology, Computational biology and bioinformatics, Tumour immunology, Systems analysis, Immunotherapy

Published Open-Access

yes

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