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Record W2990428470 · doi:10.1186/s13059-019-1824-y

DNA methylation aging clocks: challenges and recommendations

2019· review· en· W2990428470 on OpenAlexfundno aff
Christopher G. Bell, Robert Lowe, Peter D. Adams, Andrea Baccarelli, Stephan Beck, Jordana T. Bell, Brock C. Christensen, Vadim N. Gladyshev, Bastiaan T. Heijmans, Steve Horvath, Trey Ideker, Jean‐Pierre J. Issa, Karl T. Kelsey, Riccardo E. Marioni, Wolf Reik, Caroline L. Relton, Leonard C. Schalkwyk, Andrew E. Teschendorff, Wolfgang Wagner, Kang Zhang, Vardhman K. Rakyan

Bibliographic record

VenueGenome biology · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsnot available
FundersInstitute of GeneticsNational Institute of Environmental Health SciencesNational Institute of Allergy and Infectious DiseasesNational Institute of Mental HealthNational Institute for Health and Care ResearchNational Institute on AgingDepartment of Health and Social CareMedical Research CouncilNational Institutes of HealthRWTH Aachen UniversityEconomic and Social Research CouncilUniversity College London Hospitals NHS Foundation TrustNederlandse Organisatie voor Wetenschappelijk OnderzoekAlzheimer’s Research UKCancer Research UKBiotechnology and Biological Sciences Research CouncilZonMwJoint Programming Initiative A healthy diet for a healthy lifeDeutsche KrebshilfeNational Natural Science Foundation of ChinaNational Cancer InstituteUniversity of BristolDeutsche ForschungsgemeinschaftDiabetes UKBundesministerium für Bildung und ForschungNational Science FoundationUCLH Biomedical Research CentreAmerican Association for Cancer Research
KeywordsBiologyEpigenomicsDNA methylationEpigeneticsHuman geneticsEvolutionary biologyComputational biologyLongevityBiomarkerMolecular clockPopulationGeneticsCpG siteGeneGene expressionDemography

Abstract

fetched live from OpenAlex

Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual.

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)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.008
Open science0.0040.002
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0080.007

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 head0.088
GPT teacher head0.363
Teacher spread0.275 · 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 designNot applicable
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

Citations1,103
Published2019
Admission routes1
Has abstractyes

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