Predicting age from the transcriptome of human dermal fibroblasts

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 t...

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Detalles Bibliográficos
Autores principales: Fleischer, Jason G., Schulte, Roberta, Tsai, Hsiao H., Tyagi, Swati, Ibarra, Arkaitz, Shokhirev, Maxim N., Huang, Ling, Hetzer, Martin W., Navlakha, Saket
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Short Report
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6300908/
https://www.ncbi.nlm.nih.gov/pubmed/30567591
http://dx.doi.org/10.1186/s13059-018-1599-6
Descripción
Sumario: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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1599-6) contains supplementary material, which is available to authorized users.

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