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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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:	bioinformatics bioinformatics > genomics and proteomics > genetics & nucleic acid processing bioinformatics > genomics and proteomics Investigative techniques and equipment bioinformatics > computational biology > algorithms Investigative techniques and equipment > assays organs, tissues, organelles, cell types and functions > cell types and functions > cell types organs, tissues, organelles, cell types and functions > cell types and functions > cell types organs, tissues, organelles, cell types and functions > cell types and functions > cell types organs, tissues, organelles, cell types and functions > cell types and functions bioinformatics > computational biology 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 organs, tissues, organelles, cell types and functions Investigative techniques and equipment > assays > RNA-seq bioinformatics > genomics and proteomics > genetics & nucleic acid processing > transcriptomes
CSHL Authors:	Navlakha, Saket
Communities:	CSHL labs > Navlakha lab
Depositing User:	Matthew Dunn
Date:	20 December 2018
Date Deposited:	06 Nov 2019 17:19
Last Modified:	20 Feb 2024 18:54
PMCID:	PMC6300908
Related URLs:	Publisher
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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