Evidence map›Paper›PMID 41825448›Full record

ArticleCell reports methods2026

Ontology-aware DNA methylation classification with a curated atlas of human tissues and cell types.

Mirae Kim, Ruth Dannenfelser, Yufei Cui, Genevera Allen, Vicky Yao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Mirae KimDepartment of Computer Science, Rice University, Houston, TX 77005, USA.
Ruth DannenfelserDepartment of Computer Science, Rice University, Houston, TX 77005, USA.
Yufei CuiDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, US.
Genevera AllenDepartment of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Institute for Mind, Brain and Behavior, Columbia University, New York, NY 10027, USA; Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA.
Vicky YaoDepartment of Computer Science, Rice University, Houston, TX 77005, USA; Ken Kennedy Institute, Rice University, Houston, TX 77005, USA; Rice Synthetic Biology Institute, Rice University, Houston, TX 77005, USA. Electronic address: vy@rice.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA methylation is a key regulatory mechanism reflecting both short- and long-term biological stimuli. While it has been widely used to study aging through disease-associated methylation shifts, its potential for revealing tissue-specific shifts remains underexplored due to the lack of comprehensive reference atlases with correspondingly systematic analysis framework. To address this, we assemble the largest and most diverse atlas of healthy human tissue and cells profiled by 450K arrays, totaling 16,959 samples across 86 tissues and cell types. Using this resource, we introduce an ontology-aware classification framework that identifies robust CpG features linked to tissue and cell identity and incorporates known anatomical and functional relationships. Through minipatch learning, we distill 190 CpGs that support accurate multilabel classification and validate the approach with ontology-based label transfer to 31 unseen tissue and cell types.

Indexed as

DNA MethylationCpG IslandsEpigenomicsHumansMachine LearningOrgan Specificitycell-type identificationCP: geneticsCpG feature selectionCP: systems biologyDNA methylationepigenomicsmachine learningmethylation biomarkersmultilabel classificationontologyreference atlastissue classification

Identifiers

PMID41825448
PMCPMC13030963

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Registered trials

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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.