Predicting age from the transcriptome of human dermal fibroblasts
| Autor: | Ling Huang, Martin W. Hetzer, Saket Navlakha, Swati Tyagi, Arkaitz Ibarra, Jason G. Fleischer, Hsiao H. Tsai, Maxim N. Shokhirev, Roberta Schulte |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Adult Aging lcsh:QH426-470 Adolescent Biological age Short Report Skin fibroblasts RNA-Seq Biology Bioinformatics Transcriptome Machine Learning 03 medical and health sciences Young Adult Progeria Biomarkers of aging medicine Humans Child lcsh:QH301-705.5 Aged Aged 80 and over Gene Expression Profiling Genomics Biomarker Fibroblasts Middle Aged medicine.disease Ensemble learning Human genetics Biomarker (cell) lcsh:Genetics 030104 developmental biology lcsh:Biology (General) Child Preschool RNA-seq Ensemble classifiers |
| Zdroj: | Genome Biology Genome Biology, Vol 19, Iss 1, Pp 1-8 (2018) |
| ISSN: | 1474-760X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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