Predicting age from the transcriptome of human dermal fibroblasts.
Jason G FleischerRoberta SchulteHsiao H TsaiSwati TyagiArkaitz IbarraMaxim N ShokhirevLing HuangMartin W HetzerSaket NavlakhaPublished in: Genome biology (2018)
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.
Keyphrases
- rna seq
- genome wide
- end stage renal disease
- single cell
- machine learning
- ejection fraction
- chronic kidney disease
- healthcare
- peritoneal dialysis
- gene expression
- prognostic factors
- public health
- mental health
- patient reported outcomes
- health information
- deep learning
- pluripotent stem cells
- social media
- patient reported
- human health