Evidence map›Paper›PMID 37524436›Full record

ArticleGenome research2023

A transcriptome-based single-cell biological age model and resource for tissue-specific aging measures.

Shulin Mao, Jiayu Su, Longteng Wang, Xiaochen Bo, Cheng Li, Hebing Chen

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed, 29 citations in OpenAlex.

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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

6 authors at 2 institutions in 2 countries.

Shulin Mao *Yuanpei College, Peking University, Beijing 100871, China.
Jiayu Su *Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China.
Longteng WangCenter for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China.
Xiaochen BoInstitute of Health Service and Transfusion Medicine, Beijing 100850, China.
Cheng LiCenter for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China; chenhb@bmi.ac.cn cheng_li@pku.edu.cn.
Hebing ChenInstitute of Health Service and Transfusion Medicine, Beijing 100850, China; chenhb@bmi.ac.cn cheng_li@pku.edu.cn.
Peking University · CNChina National Health Development Research Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurately measuring biological age is crucial for improving healthcare for the elderly population. However, the complexity of aging biology poses challenges in how to robustly estimate aging and interpret the biological significance of the traits used for estimation. Here we present SCALE, a statistical pipeline that quantifies biological aging in different tissues using explainable features learned from literature and single-cell transcriptomic data. Applying SCALE to the "Mouse Aging Cell Atlas" (

Indexed as

AgingTranscriptomeAnimalsGene Expression ProfilingMiceModels, BiologicalPhenotype

Identifiers

PMID37524436
PMCPMC10547252
OpenAlexW4385429015

What OpenQuestion holds

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LicenceCC BY-NC
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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.