ArticleNPJ aging and mechanisms of disease2021
Modeling transcriptomic age using knowledge-primed artificial neural networks.
Article in NPJ aging and mechanisms of disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Article
- Pasta, a Versatile Transcriptomic Clock, Maps the Chemical and Genetic Determinants of Aging and Rejuvenation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
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- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Skin aging: mechanisms, evaluation, and rejuvenation.The EMBO journal · 2026Review
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- A lipidomic based metabolic age score for monitoring the effects of lifestyle and diet on metabolic disease risk.Research square · 2026Article
- The Evolving Landscape of Clinical Aging Clocks: From Epigenetic to Multi-Omics Integration.Aging cell · 2026Review
- Article
- Review
- An snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging.Scientific reports · 2026Article
- Artificial intelligence approaches in biological age prediction: current status and challenges.British journal of biomedical science · 2026Review
- Improving our understanding of the biology of aging: findings from the Age-It Research Program.The journals of gerontology. Series B, Psychological sciences and social sciences · 2025Article
- Lymphoma accelerates T cell and tissue aging.Cancer cell · 2025Article
- Development of a novel transcriptomic measure of aging: Transcriptomic Mortality-risk Age (TraMA).Aging · 2025Article
- Dynamic and reversible transcriptomic age shifts induced by COVID-19 in Korean whole blood.Aging · 2025Article
- A Standardized Protocol for Mouse Longevity Studies in Preclinical Drug Development.Aging and disease · 2025Article
- Article
- Article
- Epigenetic ageing clocks: statistical methods and emerging computational challenges.Nature reviews. Genetics · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of 'age clocks', machine learning models predicting age from biological data, has been a major milestone in the search for reliable markers of biological age and has since become an invaluable tool in aging research. However, beyond their unquestionable utility, current clocks offer little insight into the molecular biological processes driving aging, and their inner workings often remain non-transparent. Here we propose a new type of age clock, one that couples predictivity with interpretability of the underlying biology, achieved through the incorporation of prior knowledge into the model design. The clock, an artificial neural network constructed according to well-described biological pathways, allows the prediction of age from gene expression data of skin tissue with high accuracy, while at the same time capturing and revealing aging states of the pathways driving the prediction. The model recapitulates known associations of aging gene knockdowns in simulation experiments and demonstrates its utility in deciphering the main pathways by which accelerated aging conditions such as Hutchinson-Gilford progeria syndrome, as well as pro-longevity interventions like caloric restriction, exert their effects.
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Registered trials
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