Predicting age from the transcriptome of human dermal fibroblasts

Fleischer, J. G., Schulte, R., Tsai, H. H., Tyagi, S., Ibarra, A., Shokhirev, M. N., Huang, L., Hetzer, M. W., Navlakha, S. (December 2018) Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol, 19 (1). p. 221. ISSN 1474-7596 (Public Dataset)

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URL: https://www.ncbi.nlm.nih.gov/pubmed/?term=30567591...
DOI: 10.1186/s13059-018-1599-6

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.

Item Type: Paper
Subjects: organs, tissues, organelles, cell types and functions > cell types and functions > cell types > fibroblasts
organs, tissues, organelles, cell types and functions > cell types and functions > cell types > fibroblasts
organs, tissues, organelles, cell types and functions > cell types and functions > cell types > fibroblasts
bioinformatics > computational biology > algorithms > machine learning
Investigative techniques and equipment > assays > RNA-seq
CSHL Authors:
Communities: CSHL labs > Navlakha lab
Depositing User: Matthew Dunn
Date: 20 December 2018
Date Deposited: 06 Nov 2019 17:19
Last Modified: 06 Nov 2019 17:19
PMCID: PMC6300908
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Dataset ID:
  • Supplement https://link.springer.com/article/10.1186/s13059-018-1599-6
URI: https://repository.cshl.edu/id/eprint/38635

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