ArticleScientific reports2024
Biologically informed deep learning for explainable epigenetic clocks.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 34 citations in OpenAlex.
- Hybrid CNN and multi-head attention model for analyzing epigenetic mechanisms and gene expression across fungal phylogenetic distances.iScience · 2026Article
- Skin aging: mechanisms, evaluation, and rejuvenation.The EMBO journal · 2026Review
- Article
- DNMT/TET Imbalance and Network-Level DNA Methylation Remodeling in Ovarian Aging: Mechanistic Perspectives.Biology · 2026Review
- CLinNET: An Interpretable and Uncertainty-Aware Deep Learning Framework for Multi-Modal Clinical Genomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The quantified immune-aging dysregulation index: a large-language model-powered method for annotating and quantifying systems-level dysregulation.Frontiers in artificial intelligence · 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
- Turning back time: a comprehensive list of interventions that decrease next-generation epigenetic aging clocks in humans.Frontiers in genetics · 2026Review
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- Misalignment of age clocks.GeroScience · 2025Article
- Metabolomic-based aging clocks.npj metabolic health and disease · 2025Review
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- Implications From the Analogous Relationship Between Evolutionary and Learning Processes.BioEssays : news and reviews in molecular, cellular and developmental biology · 2025Review
- Article
- Epigenetic ageing clocks: statistical methods and emerging computational challenges.Nature reviews. Genetics · 2025Review
- Quantification of Epigenetic Aging in Public Health.Annual review of public health · 2025Review
- Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging.Scientific reports · 2025Article
- Environmental DNA Epigenetics Accurately Predicts the Age of Cultured Fish Larvae.Ecology and evolution · 2025Article
- Deep learning and generative artificial intelligence in aging research and healthy longevity medicine.Aging · 2025Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, however the key functions and biological processes may govern ageing are still not understood. Although age predictors called epigenetic clocks can accurately estimate the biological age of an individual based on cellular DNA methylation, their models have limited ability to explain the prediction algorithm behind and underlying key biological processes controlling ageing. Here we present XAI-AGE, a biologically informed, explainable deep neural network model for accurate biological age prediction across multiple tissue types. We show that XAI-AGE outperforms the first-generation age predictors and achieves similar results to deep learning-based models, while opening up the possibility to infer biologically meaningful insights of the activity of pathways and other abstract biological processes directly from the model.
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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.