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Record W4417449077 · doi:10.3389/fragi.2025.1682873

Methylation and algorithms in biological aging: a scoping review

2025· article· en· W4417449077 on OpenAlexafffund
Alison Ziesel, Jennifer Reeves, Αναστασία Μαλλίδου, Lorelei Newton, Ryan E. Rhodes, Jie Zhang, Theone Paterson, Hosna Jabbari

Bibliographic record

VenueFrontiers in Aging · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity of VictoriaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDNA methylationEpigeneticsIdentification (biology)Process (computing)MethylationBiological data

Abstract

fetched live from OpenAlex

The role of DNA methylation in the process of biological aging is a particularly active area of research, where methylation changes may be a consequence or a driver in the deviation between biological and chronological age. We employ a scoping review strategy to analyze the results of 435 relevant research papers, 167 of which employed methylation-based strategies to interrogate biological age. Our work details the progression and refinement of these strategies over time, as well as the development of novel methylation-based clocks and algorithmic methods. Our chosen review strategy allows for the identification of research findings consistent and discordant with one another, as well as focusing on exciting, potential research areas regarding measurement, calculation, and assessment of epigenetic biological age.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.134
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.032
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.134
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0210.021
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.319
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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