IDEAL-Age: an interpretable deep learning framework for single-cell resolution profiling of immunological aging.

Authors

Yin Xu, Zhengchao Luo, Kai He, Feifan Zhang, Yawei Zhang, Jinzhuo Wang, Han Wen, Yongge Li, Dali Han

Published in

Genome biology. Volume 27. Issue 1. Jul 27, 2026. Epub Jul 27, 2026.

Abstract

Immunosenescence increases susceptibility to infection and reduces vaccine responsiveness, yet bulk transcriptomic clocks obscure the cellular heterogeneity underlying this process. Here, we present IDEAL-Age, an interpretable deep learning framework that operates directly on single-cell PBMC transcriptomes. Benchmarking against 35 methods across independent cohorts demonstrates superior predictive performance. The framework's interpretability uncovers linear and non-linear gene contribution trajectories that reveal phase-specific physiological transitions, and identifies youth-associated or aging-associated cellular roles. Application to systemic lupus erythematosus reveals accelerated immunological aging driven by interferon-associated monocyte shifts. IDEAL-Age establishes a high-resolution computational framework for deciphering systemic immune aging.

PMID:
42503507
Bibliographic data and abstract were imported from PubMed on 27 Jul 2026.

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