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Predicting age from the transcriptome of human dermal fibroblasts |
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| Authors: | Jason G. Fleischer Roberta Schulte Hsiao H. Tsai Swati Tyagi Arkaitz Ibarra Maxim N. Shokhirev Ling Huang Martin W. Hetzer Saket Navlakha |
| Affiliation: | 1.Integrative Biology Laboratory,The Salk Institute for Biological Studies,La Jolla,USA;2.Molecular and Cell Biology Laboratory,The Salk Institute for Biological Studies,La Jolla,USA;3.Molecular Stethoscope Inc.,San Diego,USA;4.Bioinformatics Core,The Salk Institute for Biological Studies,La Jolla,USA |
| 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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