Evidence map›Paper›PMID 39806006›Full record

ReviewNature reviews. Genetics2025

Epigenetic ageing clocks: statistical methods and emerging computational challenges.

Andrew E Teschendorff, Steve Horvath

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 128 papers.

0numbers the graph read from it
0cells of the map it votes in
128citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

128 citing papers in PubMed.

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  13. The Hallmarks of Aging: From Molecular Mechanisms to Clinical Translation.International journal of molecular sciences · 2026
    Review
  14. Article
  15. Article
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68 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Andrew E TeschendorffCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. andrew@sinh.ac.cn.ORCID http://orcid.org/0000-0001-7410-6527
Steve HorvathAltos Labs, Cambridge, UK. shorvath@altoslabs.com.ORCID http://orcid.org/0000-0002-4110-3589

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past decade, epigenetic clocks have emerged as powerful machine learning tools, not only to estimate chronological and biological age but also to assess the efficacy of anti-ageing, cellular rejuvenation and disease-preventive interventions. However, many computational and statistical challenges remain that limit our understanding, interpretation and application of epigenetic clocks. Here, we review these computational challenges, focusing on interpretation, cell-type heterogeneity and emerging single-cell methods, aiming to provide guidelines for the rigorous construction of interpretable epigenetic clocks at cell-type and single-cell resolution.

Indexed as

AgingBiological ClocksComputational BiologyEpigenesis, GeneticEpigenomicsAnimalsHumansMachine LearningSingle-Cell Analysis

Identifiers

PMID39806006

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.