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Genome Biol. 2018 Dec 20;19(1):221. doi: 10.1186/s13059-018-1599-6.

Predicting age from the transcriptome of human dermal fibroblasts.

Author information

1
Integrative Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
2
Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
3
Molecular Stethoscope Inc., San Diego, CA, 92121, USA.
4
Bioinformatics Core, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
5
Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA. hetzer@salk.edu.
6
Integrative Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA. navlakha@salk.edu.

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.

KEYWORDS:

Aging; Biological age; Biomarker; Ensemble classifiers; Machine learning; RNA-seq; Skin fibroblasts

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