Evidence map›Paper›PMID 30567591›Full record

ArticleGenome biology2018

Predicting age from the transcriptome of human dermal fibroblasts.

Jason G Fleischer, Roberta Schulte, Hsiao H Tsai, Swati Tyagi, Arkaitz Ibarra, Maxim N Shokhirev, Ling Huang, Martin W Hetzer, Saket Navlakha

Abstract readEvaluation Study
In one paragraph

Article in Genome biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 143 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
143citing papers in PubMed, 4 pooled it
–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

143 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  8. AcutebioRxiv : the preprint server for biology · 2026
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  20. Cellular Senescence of Patient-derived Fibroblasts Reveals the Mid-old Stage as a Critical Window for Transcriptomic Signatures Linked to Alzheimer's Disease Biomarkers and Classification.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2026
    Article

83 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

9 authors.

Jason G FleischerIntegrative Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
Roberta SchulteMolecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
Hsiao H TsaiMolecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
Swati TyagiMolecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
Arkaitz IbarraMolecular Stethoscope Inc., San Diego, CA, 92121, USA.
Maxim N ShokhirevBioinformatics Core, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
Ling HuangBioinformatics Core, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
Martin W HetzerMolecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA. hetzer@salk.edu.
Saket NavlakhaIntegrative Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA. navlakha@salk.edu.ORCID 0000-0002-5505-9718

Funding

Viral Vector Core (VVC)P30CA014195 · NCI · SALK INSTITUTE FOR BIOLOGICAL STUDIES · PI Ye Zheng · 1985 to 2026
$82.8M
The role of long-lived proteins in the survival of nerve cellsR01NS096786 · NINDS · SALK INSTITUTE FOR BIOLOGICAL STUDIES · PI HETZER, MARTIN W · 2015 to 2019
$3.6M
CRCNS: Common algorithmic strategies used by the brain for labeling points in high-dimensional spaceR01DC017695 · NIDCD · SALK INSTITUTE FOR BIOLOGICAL STUDIES · PI NAVLAKHA, SAKET · 2018 to 2020
$1.3M
NCI NIH HHS P30 CA014195NIDCD NIH HHS R01 DC017695NIH HHS 1R01DC017695NIH HHS R01-NS096786NINDS NIH HHS R01 NS096786
6 · The paper itself

Abstract

Biomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases. We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the transcriptome. We develop an ensemble machine learning method that predicts age to a median error of 4 years, outperforming previous methods used to predict age. The ensemble was further validated by testing it on ten progeria patients, and our method is the only one that predicts accelerated aging in these patients.

Indexed as

Gene Expression ProfilingTranscriptomeAdolescentAdultAgedAged, 80 and overAgingChildChild, PreschoolFibroblastsGenomicsHumansMachine LearningMiddle AgedProgeriaYoung AdultAgingBiological ageBiomarkerEnsemble classifiersMachine learningRNA-seqSkin fibroblasts

Identifiers

PMID30567591
PMCPMC6300908

What OpenQuestion holds

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LicenceCC BY
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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.