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Cited by 232Genetics, Aging, and Longevity in Model OrganismsSingle-cell and spatial transcriptomicsGene expression and cancer classification

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

Predicting age from the transcriptome of human dermal fibroblasts

Open access
Fleischer, Jason G., Schulte, Roberta, Tsai, Hsiao H., Tyagi, Swati, Ibarra, Arkaitz, Shokhirev, Maxim N., Huang, Ling, Hetzer, Martin W., Navlakha, Saket·2018·Genome Biology
DOI 10.1186/s13059-018-1599-6PMC PMC6300908
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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.

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Fig. 1 Predicting age from gene expression data. Rows from top to bottom show age prediction results for LDA Ensemble with 20-year age bins, elastic net, linear regression, and support vector regression. Model parameters are shown in Table 1. Column (A): Leave-one-out cross-validation predictions for 133 healthy individuals. Dots are plotted for each individual showing predicted age (y-axis) vs. true age (x-axis), with a line of best fit overlaid, and a shadow showing the 95% confidence interval of that line determined through bootstrap resampling of the dots. Text on the bottom of each panel shows performance metrics of mean absolute error (MAE), median absolute error (MED), and R2 goodness-of-fit for the line of best fit. The dotted line is the ideal line, where true age equals predicted age. Column (B): The effect of training set size (x-axis) on the mean absolute error of the ensemble (y-axis). The slope of the best fit line indicates the rate at which age prediction error would decrease with additional samples. Dots indicate mean absolute error from each fold of 2 × 10 cross-validation (y-axis) for varying sizes of random subset of the data (x-axis). A line of best fit and 95% confidence interval is shown. Column (C): Box plots of age predictions of progeria patients (red) and leave-one-out cross-validation predictions of age-matched healthy controls (blue). Box limits denote 25th and 75th percentiles, line is median, whiskers are 1.5× interquartile range, and dots are predictions outside the whisker’s range. The ensemble method is the only method that predicts significantly higher ages for progeria patients. Progeria patients: n = 10, mean ± std. of true age 5.5 ± 2.4; age-matched controls: n = 12, mean ± std. of true age 5.0 ± 2.9
Fig. 1 Predicting age from gene expression data. Rows from top to bottom show age prediction results for LDA Ensemble with 20-year age bins, elastic net, linear regression, and support vector regression. Model parameters are shown in Table 1. Column (A): Leave-one-out cross-validation predictions for 133 healthy individuals. Dots are plotted for each individual showing predicted age (y-axis) vs. true age (x-axis), with a line of best fit overlaid, and a shadow showing the 95% confidence interval of that line determined through bootstrap resampling of the dots. Text on the bottom of each panel shows performance metrics of mean absolute error (MAE), median absolute error (MED), and R2 goodness-of-fit for the line of best fit. The dotted line is the ideal line, where true age equals predicted age. Column (B): The effect of training set size (x-axis) on the mean absolute error of the ensemble (y-axis). The slope of the best fit line indicates the rate at which age prediction error would decrease with additional samples. Dots indicate mean absolute error from each fold of 2 × 10 cross-validation (y-axis) for varying sizes of random subset of the data (x-axis). A line of best fit and 95% confidence interval is shown. Column (C): Box plots of age predictions of progeria patients (red) and leave-one-out cross-validation predictions of age-matched healthy controls (blue). Box limits denote 25th and 75th percentiles, line is median, whiskers are 1.5× interquartile range, and dots are predictions outside the whisker’s range. The ensemble method is the only method that predicts significantly higher ages for progeria patients. Progeria patients: n = 10, mean ± std. of true age 5.5 ± 2.4; age-matched controls: n = 12, mean ± std. of true age 5.0 ± 2.9CC BY · PubMed Central

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Show all 26 references

Research works printed but not linked: 2

  1. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd edition. New York: Springer; 2008.
  2. Shokirev MN. Predicting age from the transcriptome of human dermal fibroblasts. Data sets. [Internet]. Gene Expression Omnibus; 2018. Available from: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113957 . Accessed 12 Dec 2018.

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Metadata

Authors
Fleischer, J. G., Schulte, R., Tsai, H. H., Tyagi, S., Ibarra, A., Shokhirev, M. N., Huang, L., Hetzer, M. W., & Navlakha, S.
Affiliations
Salk Institute for Biological Studies (US)
Corresponding author
  • Hetzer, Martin W.
  • Navlakha, Saket
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2018-12
Volume
19
Issue
1
Pages
221-221
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-018-1599-6
PMC
PMC6300908
OpenAlex
W2905232298
Cited by
232
References
26
Citations by year
2026: 25 · 2025: 34 · 2024: 36 · 2023: 43 · 2022: 27 · 2021: 33 · 2020: 23 · 2019: 10 · 2018: 1
FWCI
6.58
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Institutes of Health (R01-NS096786, 1R01DC017695)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
age prediction, aging biomarkers, transcriptome, human dermal fibroblasts, RNA-seq, ensemble learning, Hutchinson-Gilford progeria syndrome, accelerated aging, age-related diseases
MeSH terms
Machine Learning, Adolescent, Adult, Aged, Aged, 80 and over, Aging, Child, Child, Preschool, Fibroblasts, Humans, Middle Aged, Progeria, Gene Expression Profiling, Genomics, Young Adult, Transcriptome

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