{"intvolume":"        19","volume":19,"year":"2018","citation":{"ieee":"J. G. Fleischer <i>et al.</i>, “Predicting age from the transcriptome of human dermal fibroblasts,” <i>Genome Biology</i>, vol. 19. BioMed Central, 2018.","mla":"Fleischer, Jason G., et al. “Predicting Age from the Transcriptome of Human Dermal Fibroblasts.” <i>Genome Biology</i>, vol. 19, 221, BioMed Central, 2018, doi:<a href=\"https://doi.org/10.1186/s13059-018-1599-6\">10.1186/s13059-018-1599-6</a>.","short":"J.G. Fleischer, R. Schulte, H.H. Tsai, S. Tyagi, A. Ibarra, M.N. Shokhirev, L. Huang, M. Hetzer, S. Navlakha, Genome Biology 19 (2018).","ama":"Fleischer JG, Schulte R, Tsai HH, et al. Predicting age from the transcriptome of human dermal fibroblasts. <i>Genome Biology</i>. 2018;19. doi:<a href=\"https://doi.org/10.1186/s13059-018-1599-6\">10.1186/s13059-018-1599-6</a>","apa":"Fleischer, J. G., Schulte, R., Tsai, H. H., Tyagi, S., Ibarra, A., Shokhirev, M. N., … Navlakha, S. (2018). Predicting age from the transcriptome of human dermal fibroblasts. <i>Genome Biology</i>. BioMed Central. <a href=\"https://doi.org/10.1186/s13059-018-1599-6\">https://doi.org/10.1186/s13059-018-1599-6</a>","chicago":"Fleischer, Jason G., Roberta Schulte, Hsiao H. Tsai, Swati Tyagi, Arkaitz Ibarra, Maxim N. Shokhirev, Ling Huang, Martin Hetzer, and Saket Navlakha. “Predicting Age from the Transcriptome of Human Dermal Fibroblasts.” <i>Genome Biology</i>. BioMed Central, 2018. <a href=\"https://doi.org/10.1186/s13059-018-1599-6\">https://doi.org/10.1186/s13059-018-1599-6</a>.","ista":"Fleischer JG, Schulte R, Tsai HH, Tyagi S, Ibarra A, Shokhirev MN, Huang L, Hetzer M, Navlakha S. 2018. Predicting age from the transcriptome of human dermal fibroblasts. Genome Biology. 19, 221."},"article_type":"original","language":[{"iso":"eng"}],"_id":"11064","article_number":"221","doi":"10.1186/s13059-018-1599-6","author":[{"full_name":"Fleischer, Jason G.","first_name":"Jason G.","last_name":"Fleischer"},{"first_name":"Roberta","last_name":"Schulte","full_name":"Schulte, Roberta"},{"first_name":"Hsiao H.","last_name":"Tsai","full_name":"Tsai, Hsiao H."},{"first_name":"Swati","last_name":"Tyagi","full_name":"Tyagi, Swati"},{"first_name":"Arkaitz","last_name":"Ibarra","full_name":"Ibarra, Arkaitz"},{"full_name":"Shokhirev, Maxim N.","first_name":"Maxim N.","last_name":"Shokhirev"},{"first_name":"Ling","last_name":"Huang","full_name":"Huang, Ling"},{"full_name":"HETZER, Martin W","id":"86c0d31b-b4eb-11ec-ac5a-eae7b2e135ed","last_name":"HETZER","first_name":"Martin W","orcid":"0000-0002-2111-992X"},{"last_name":"Navlakha","first_name":"Saket","full_name":"Navlakha, Saket"}],"publication":"Genome Biology","external_id":{"pmid":["30567591"]},"publication_identifier":{"issn":["1474-760X"]},"title":"Predicting age from the transcriptome of human dermal fibroblasts","quality_controlled":"1","status":"public","oa":1,"oa_version":"Published Version","date_created":"2022-04-07T07:45:40Z","user_id":"72615eeb-f1f3-11ec-aa25-d4573ddc34fd","publisher":"BioMed Central","scopus_import":"1","extern":"1","date_published":"2018-12-20T00:00:00Z","publication_status":"published","main_file_link":[{"url":"https://doi.org/10.1186/s13059-018-1599-6","open_access":"1"}],"abstract":[{"lang":"eng","text":"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."}],"type":"journal_article","day":"20","month":"12","article_processing_charge":"No","pmid":1,"date_updated":"2022-07-18T08:32:34Z"}