ArticleAging and disease2021
DeepMAge: A Methylation Aging Clock Developed with Deep Learning.
Article in Aging and disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
78 citing papers in PubMed, 2 syntheses or guidelines pooled it, 150 citations in OpenAlex.
- Pooled it
- Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI.GigaScience · 2022Pooled it
- Hematopoietic stem cell aging: a review of transcriptional and multi-omics insights and potential paths for AI integration.Experimental & molecular medicine · 2026Review
- Article
- Epigenetic mechanisms of gingival aging: biological basis, periodontal implications, and therapeutic perspectives.Odontology · 2026Review
- Enhancing the performance and interpretability of epigenetic clocks.Nucleic acids research · 2026Article
- Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.NPJ digital medicine · 2026Article
- Article
- The Evolving Landscape of Clinical Aging Clocks: From Epigenetic to Multi-Omics Integration.Aging cell · 2026Review
- Predicting Post-Radiotherapy Epigenetic Age Acceleration From Pre-Treatment Data Using a Machine Learning Framework in Head and Neck Cancer Patients.Cancer medicine · 2026Article
- From the lab to lifestyle: epigenetic clocks in personalized aging and health.Biogerontology · 2026Review
- BRIDGING THE GAP: ENHANCING THE GENERALIZABILITY OF EPIGENETIC CLOCKS THROUGH TRANSFER LEARNING.The annals of applied statistics · 2026Article
- An Evolutionary Metric for Estimating PhyloAges from Bulk Sequencing of Hematopoietic Stem Cells Reveals the Tempo of Blood Aging in Cancer and Longevity.Journal of molecular evolution · 2026Article
- MicroBayesAge: a maximum likelihood approach to predict epigenetic age using microarray data.GeroScience · 2026Article
- Methods for analysing wildlife DNA methylation data.Conservation physiology · 2026Article
- Artificial intelligence approaches in biological age prediction: current status and challenges.British journal of biomedical science · 2026Review
- Do we actually need aging clocks?npj aging · 2025Review
- KoMethylNet: a novel epigenetic clock based on neural network analysis of DNA methylation data and epigenetic age acceleration in a Korean population.BMC medicine · 2025Article
- Microvascular Health as a Key Determinant of Organismal Aging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Review
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA methylation aging clocks have become an invaluable tool in biogerontology research since their inception in 2013. Today, a variety of machine learning approaches have been tested for the purpose of predicting human age based on molecular-level features. Among these, deep learning, or neural networks, is an especially promising approach that has been used to construct accurate clocks using blood biochemistry, transcriptomics, and microbiomics data-feats unachieved by other algorithms. In this article, we explore how deep learning performs in a DNA methylation setting and compare it to the current industry standard-the 353 CpG clock published in 2013. The aging clock we are presenting (DeepMAge) is a neural network regressor trained on 4,930 blood DNA methylation profiles from 17 studies. Its absolute median error was 2.77 years in an independent verification set of 1,293 samples from 15 studies. DeepMAge shows biological relevance by assigning a higher predicted age to people with various health-related conditions, such as ovarian cancer, irritable bowel diseases, and multiple sclerosis.
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What OpenQuestion holds
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.