Predicting age from the transcriptome of human dermal fibroblasts

被引：0
|
作者：
Jason G. Fleischer
Roberta Schulte
Hsiao H. Tsai
Swati Tyagi
Arkaitz Ibarra
Maxim N. Shokhirev
Ling Huang
Martin W. Hetzer
Saket Navlakha
机构：
[1] The Salk Institute for Biological Studies,Integrative Biology Laboratory
[2] The Salk Institute for Biological Studies,Molecular and Cell Biology Laboratory
[3] Molecular Stethoscope Inc.,Bioinformatics Core
[4] The Salk Institute for Biological Studies,undefined
来源：
Genome Biology | / 19卷
关键词：
Biological age; Skin fibroblasts; Machine learning; Ensemble classifiers; RNA-seq; Aging; Biomarker;
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摘要：
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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