ArticleFrontiers in immunology2023
Small immunological clocks identified by deep learning and gradient boosting.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Proteomic Markers of Aging and Longevity: A Systematic Review.International journal of molecular sciences · 2024Pooled it
- The long-lived immune system of centenarians.Nature reviews. Immunology · 2026Review
- Blood biochemical and gut microbiotic neural network models forecasting human biological age.Aging · 2026Article
- Neurosenescence, inflammaging and neuroinflammation in neurodegenerative disorders.Frontiers in aging · 2026Review
- Towards a personalized perspective on gliomas: an epigenetic-immuno-inflammatory aging framework.Frontiers in immunology · 2026Review
- Association of an inflammaging score based on IL-6, IL-10 and CXCL9, and frailty with long-term mortality in hospitalized older adults.Immunity & ageing : I & A · 2025Article
- Toward precision interventions and metrics of inflammaging.Nature aging · 2025Review
- Nonuniversality of inflammaging across human populations.Nature aging · 2025Article
- Understanding and improving vaccine efficacy in older adults.Nature aging · 2025Review
- EpInflammAge: Epigenetic-Inflammatory Clock for Disease-Associated Biological Aging Based on Deep Learning.International journal of molecular sciences · 2025Article
- Inflammaging Markers in the Extremely Cold Climate: A Case Study of Yakutian Population.International journal of molecular sciences · 2024Article
- Critical review of aging clocks and factors that may influence the pace of aging.Frontiers in aging · 2024Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The aging process affects all systems of the human body, and the observed increase in inflammatory components affecting the immune system in old age can lead to the development of age-associated diseases and systemic inflammation. Results: We propose a small clock model SImAge based on a limited number of immunological biomarkers. To regress the chronological age from cytokine data, we first use a baseline Elastic Net model, gradient-boosted decision trees models, and several deep neural network architectures. For the full dataset of 46 immunological parameters, DANet, SAINT, FT-Transformer and TabNet models showed the best results for the test dataset. Dimensionality reduction of these models with SHAP values revealed the 10 most age-associated immunological parameters, taken to construct the SImAge small immunological clock. The best result of the SImAge model shown by the FT-Transformer deep neural network model has mean absolute error of 6.94 years and Pearson Conclusions: We developed an approach to construct a model of immunological age based on just 10 immunological parameters, coined SImAge, for which the FT-Transformer deep neural network model had proved to be the best choice. The model shows competitive results compared to the published studies on immunological profiles, and takes a smaller number of features as an input. Neural network architectures outperformed gradient-boosted decision trees, and can be recommended in the further analysis of immunological profiles.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.