{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T08:24:02Z","timestamp":1793780642264,"version":"4.6.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T00:00:00Z","timestamp":1764979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T00:00:00Z","timestamp":1764979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"NIH","award":["P30CA006927"],"award-info":[{"award-number":["P30CA006927"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Genomics"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Age is a major risk factor for various diseases, such as cancer, cardiovascular conditions, and neurodegenerative diseases. However, chronological age, the simple number of years one has lived, does not capture individual health differences, prompting the development of methods to accurately estimate biological age instead of relying on chronological age. One of the major molecular approaches exploits DNA methylation (DNAm), which is an essential epigenetic modifier for regulating gene expression, cell differentiation, and aging. DNAm-based aging clocks have been developed to predict biological age, but the prediction is highly dependent on training data, including organs and assay technologies. To address these clocks\u2019 high variance, we present two EnsembleAge clocks, leveraging eight previously developed DNAm clocks, harnessing the strengths of each methylome age clock, smoothing out individual variances, and providing a more robust estimation of biological age. We trained our EnsembleAge clock models using DNA methylation data from nine organs in the Genotype-Tissue Expression (GTEx) dataset. Our EnsembleNaive clock model achieved the lowest median absolute error (MeAE) of 4.04\u00a0years in whole blood. The EnsembleLR model demonstrated the lowest MeAE of 6.35\u00a0years across multiple tissues, including breast, lung, muscle, ovary, prostate, testis, and colon. The significant reduction in MeAE underscores its high practical value in clinical and forensic applications, especially in contexts where epigenetic changes are subtle. We further applied our models to four public datasets representing diverse biological applications, including administered short-term medical opioid use (GSE151485) and long-term opioid overdose (GSE164822). Our model reveals over 10\u00a0years of age acceleration in opioid-overdosed brains but no significant epigenetic age acceleration when opioid usage was well administered. Our EnsembleAge clock models are also implemented as a web service, allowing users to conveniently upload their DNA methylation data and receive predictions of their biological age. This empowers individuals to track their biological\/epigenetic age over time, mitigating the effect of variance and promoting healthy aging and a beneficial lifestyle. Our EnsembleAge clock service is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/ensemble.epiclock.app\/\" ext-link-type=\"uri\">https:\/\/ensemble.epiclock.app\/<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1186\/s12864-025-12271-9","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:14:40Z","timestamp":1764976480000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["EnsembleAge clock: a reliable and robust epigenetic age clock service reveals epigenetic age acceleration in opioid-overdosed brains"],"prefix":"10.1186","volume":"26","author":[{"given":"Akshay","family":"Anand","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yash","family":"Agarwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanisha","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mirna","family":"Ghemrawi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Glenn S.","family":"Gerhard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hayan","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,6]]},"reference":[{"key":"12271_CR1","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1016\/j.molcel.2018.08.008","volume":"71","author":"AE Field","year":"2018","unstructured":"Field AE, Robertson NA, Wang T, Havas A, Ideker T, Adams PD. DNA methylation clocks in aging: categories, causes, and consequences. Mol Cell. 2018;71:882\u201395.","journal-title":"Mol Cell"},{"key":"12271_CR2","doi-asserted-by":"publisher","first-page":"88","DOI":"10.14283\/jarlife.2024.13","volume":"13","author":"WH Lu","year":"2024","unstructured":"Lu WH. Effect of modifiable lifestyle factors on biological aging. The Journal of Aging Research & Lifestyle. 2024;13:88\u201392.","journal-title":"The Journal of Aging Research & Lifestyle"},{"key":"12271_CR3","doi-asserted-by":"publisher","DOI":"10.1111\/acel.13995","volume":"22","author":"WS Liu","year":"2023","unstructured":"Liu WS, You J, Ge YJ, Wu BS, Zhang Y, Chen SD, et al. Association of biological age with health outcomes and its modifiable factors. Aging Cell. 2023;22:e13995.","journal-title":"Aging Cell"},{"key":"12271_CR4","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1177\/1099800412444785","volume":"15","author":"KE Stephens","year":"2013","unstructured":"Stephens KE, Miaskowski CA, Levine JD, Pullinger CR, Aouizerat BE. Epigenetic regulation and measurement of epigenetic changes. Biol Res Nurs. 2013;15:373\u201381.","journal-title":"Biol Res Nurs"},{"key":"12271_CR5","doi-asserted-by":"publisher","DOI":"10.3390\/cells12081163","author":"A la Torre","year":"2023","unstructured":"la Torre A, Lo Vecchio F, Greco A. Epigenetic mechanisms of aging and aging-associated diseases. Cells. 2023. https:\/\/doi.org\/10.3390\/cells12081163.","journal-title":"Cells"},{"key":"12271_CR6","doi-asserted-by":"crossref","unstructured":"Peters TJ, Meyer B, Ryan L, Achinger-Kawecka J, Song J, Campbell EM, et al. Characterisation and reproducibility of the HumanMethylationEPIC v2.0 BeadChip for DNA methylation profiling. BMC Genomics. 2024;25:251.","DOI":"10.1186\/s12864-024-10027-5"},{"key":"12271_CR7","doi-asserted-by":"publisher","first-page":"R115","DOI":"10.1186\/gb-2013-14-10-r115","volume":"14","author":"S Horvath","year":"2013","unstructured":"Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115.","journal-title":"Genome Biol"},{"key":"12271_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2020.00171","volume":"11","author":"Y Salameh","year":"2020","unstructured":"Salameh Y, Bejaoui Y, El Hajj N. DNA methylation biomarkers in aging and age-related diseases. Front Genet. 2020;11:171.","journal-title":"Front Genet"},{"key":"12271_CR9","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.molcel.2012.10.016","volume":"49","author":"G Hannum","year":"2013","unstructured":"Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49:359\u201367.","journal-title":"Mol Cell"},{"key":"12271_CR10","doi-asserted-by":"publisher","first-page":"1758","DOI":"10.18632\/aging.101508","volume":"10","author":"S Horvath","year":"2018","unstructured":"Horvath S, Oshima J, Martin GM, Lu AT, Quach A, Cohen H, et al. Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria syndrome and ex vivo studies. Aging. 2018;10:1758\u201375.","journal-title":"Aging"},{"key":"12271_CR11","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-019-0667-1","volume":"11","author":"Q Zhang","year":"2019","unstructured":"Zhang Q, Vallerga CL, Walker RM, Lin T, Henders AK, Montgomery GW, et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Med. 2019;11:54.","journal-title":"Genome Med"},{"key":"12271_CR12","doi-asserted-by":"publisher","DOI":"10.1186\/s13148-017-0315-9","volume":"9","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Hapala J, Brenner H, Wagner W. Individual CpG sites that are associated with age and life expectancy become hypomethylated upon aging. Clin Epigenetics. 2017;9:9.","journal-title":"Clin Epigenetics"},{"key":"12271_CR13","doi-asserted-by":"publisher","first-page":"573","DOI":"10.18632\/aging.101414","volume":"10","author":"ME Levine","year":"2018","unstructured":"Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10:573\u201391.","journal-title":"Aging"},{"key":"12271_CR14","doi-asserted-by":"publisher","first-page":"303","DOI":"10.18632\/aging.101684","volume":"11","author":"AT Lu","year":"2019","unstructured":"Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging. 2019;11:303\u201327.","journal-title":"Aging"},{"key":"12271_CR15","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.73420","volume":"11","author":"DW Belsky","year":"2022","unstructured":"Belsky DW, Caspi A, Corcoran DL, Sugden K, Poulton R, Arseneault L, et al. DunedinPACE, a DNA methylation biomarker of the pace of aging. Elife. 2022;11:e73420.","journal-title":"Elife"},{"key":"12271_CR16","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-016-1064-3","volume":"17","author":"Z Yang","year":"2016","unstructured":"Yang Z, Wong A, Kuh D, Paul DS, Rakyan VK, Leslie RD, et al. Correlation of an epigenetic mitotic clock with cancer risk. Genome Biol. 2016;17:205.","journal-title":"Genome Biol"},{"key":"12271_CR17","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s12915-020-00807-2","volume":"18","author":"Y Han","year":"2020","unstructured":"Han Y, Franzen J, Stiehl T, Gobs M, Kuo CC, Nikoli\u0107 M, et al. New targeted approaches for epigenetic age predictions. BMC Biol. 2020;18:71.","journal-title":"BMC Biol"},{"key":"12271_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/s43587-023-00557-0","author":"K Ying","year":"2024","unstructured":"Ying K, Liu H, Tarkhov AE, Sadler MC, Lu AT, Moqri M, et al. Causality-enriched epigenetic age uncouples damage and adaptation. Nat Aging. 2024. https:\/\/doi.org\/10.1038\/s43587-023-00557-0.","journal-title":"Nat Aging"},{"key":"12271_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41514-022-00085-y","volume":"8","author":"LP de Lima Camillo","year":"2022","unstructured":"de Lima Camillo LP, Lapierre LR, Singh R. A pan-tissue DNA-methylation epigenetic clock based on deep learning. npj Aging. 2022;8:1\u201315.","journal-title":"npj Aging"},{"key":"12271_CR20","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-023-01161-y","volume":"15","author":"E Bernabeu","year":"2023","unstructured":"Bernabeu E, McCartney DL, Gadd DA, Hillary RF, Lu AT, Murphy L. Refining epigenetic prediction of chronological and biological age. Genome Med. 2023;15:12.","journal-title":"Genome Med"},{"key":"12271_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.celrep.2022.110459","volume":"38","author":"C Nie","year":"2022","unstructured":"Nie C, Li Y, Li R, Yan Y, Zhang D, Li T, et al. Distinct biological ages of organs and systems identified from a multi-omics study. Cell Rep. 2022;38:110459.","journal-title":"Cell Rep"},{"key":"12271_CR22","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1038\/s41588-022-01248-z","volume":"55","author":"M Oliva","year":"2023","unstructured":"Oliva M, Demanelis K, Lu Y, Chernoff M, Jasmine F, Ahsan H, et al. DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits. Nat Genet. 2023;55:112\u201322.","journal-title":"Nat Genet"},{"key":"12271_CR23","doi-asserted-by":"publisher","DOI":"10.1093\/gerona\/glad242","volume":"79","author":"DV Zavala","year":"2023","unstructured":"Zavala DV, Dzikowski N, Gopalan S, Harrington KD, Pasquini G, Mogle J, et al. Epigenetic age acceleration and chronological age: associations with cognitive performance in daily life. J Gerontol A Biol Sci Med Sci. 2023;79:glad242.","journal-title":"J Gerontol A Biol Sci Med Sci"},{"key":"12271_CR24","unstructured":"GTEx Portal. https:\/\/gtexportal.org\/home\/. Accessed 5 Aug 2025."},{"key":"12271_CR25","unstructured":"[No title]. https:\/\/arxiv.org\/pdf\/1106.1813. Accessed 30 Jul 2025."},{"key":"12271_CR26","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-14-106","volume":"14","author":"R Blagus","year":"2013","unstructured":"Blagus R, Lusa L. Smote for high-dimensional class-imbalanced data. BMC Bioinformatics. 2013;14:106.","journal-title":"BMC Bioinformatics"},{"key":"12271_CR27","unstructured":"IterativeImputer. scikit-learn. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.impute.IterativeImputer.html. Accessed 17 Oct 2025."},{"key":"12271_CR28","first-page":"119","volume":"102","author":"XL Ni","year":"2022","unstructured":"Ni XL, Yuan HP, Jiao J, Wang ZP, Su HB, Lyu Y, et al. An epigenetic clock model for assessing the human biological age of healthy aging. Zhonghua Yi Xue Za Zhi. 2022;102:119\u201324.","journal-title":"Zhonghua Yi Xue Za Zhi"},{"key":"12271_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsigen.2020.102251","volume":"46","author":"C Pan","year":"2020","unstructured":"Pan C, Yi S, Xiao C, Huang Y, Chen X, Huang D. The evaluation of seven age-related CpGs for forensic purpose in blood from Chinese Han population. Forensic Sci Int Genet. 2020;46:102251.","journal-title":"Forensic Sci Int Genet"},{"key":"12271_CR30","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1038\/s43587-024-00619-x","volume":"4","author":"DH Meyer","year":"2024","unstructured":"Meyer DH, Schumacher B. Aging clocks based on accumulating stochastic variation. Nat Aging. 2024;4:871\u201385.","journal-title":"Nat Aging"},{"key":"12271_CR31","doi-asserted-by":"crossref","unstructured":"Porter HL, Ansere VA, Undi RB, Hoolehan W, Giles CB, Brown CA, et al. Methylation Array Signals are Predictive of Chronological Age Without Bisulfite Conversion. bioRxiv. 2023.","DOI":"10.1101\/2023.12.20.572465"},{"key":"12271_CR32","unstructured":"Website. (https:\/\/dctd.cancer.gov\/data-tools-biospecimens\/biospecimens-biobanks\/finding\/gtex."},{"key":"12271_CR33","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0067378","volume":"8","author":"A Johansson","year":"2013","unstructured":"Johansson A, Enroth S, Gyllensten U. Continuous aging of the human DNA methylome throughout the human lifespan. PLoS One. 2013;8:e67378.","journal-title":"PLoS One"},{"key":"12271_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-016-2421-z","volume":"17","author":"L Kananen","year":"2016","unstructured":"Kananen L, Marttila S, Nevalainen T, Jylh\u00e4v\u00e4 J, Mononen N, K\u00e4h\u00f6nen M, et al. Aging-associated DNA methylation changes in middle-aged individuals: the Young Finns study. BMC Genomics. 2016;17:1\u201312.","journal-title":"BMC Genomics"},{"key":"12271_CR35","doi-asserted-by":"publisher","DOI":"10.1186\/s13072-024-00546-6","volume":"17","author":"N Jain","year":"2024","unstructured":"Jain N, Li JL, Tong L, Jasmine F, Kibriya MG, Demanelis K, et al. DNA methylation correlates of chronological age in diverse human tissue types. Epigenetics Chromatin. 2024;17:25.","journal-title":"Epigenetics Chromatin"},{"key":"12271_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-015-1381-z","volume":"16","author":"S Marttila","year":"2015","unstructured":"Marttila S, Kananen L, H\u00e4yrynen S, Jylh\u00e4v\u00e4 J, Nevalainen T, Hervonen A, et al. Ageing-associated changes in the human DNA methylome: genomic locations and effects on gene expression. BMC Genomics. 2015;16:1\u201317.","journal-title":"BMC Genomics"},{"key":"12271_CR37","doi-asserted-by":"publisher","first-page":"2334","DOI":"10.1038\/s41380-020-00994-y","volume":"26","author":"R Karayol","year":"2021","unstructured":"Karayol R, Medrihan L, Warner-Schmidt JL, Fait BW, Rao MN, Holzner EB, et al. Serotonin receptor 4 in the hippocampus modulates mood and anxiety. Mol Psychiatry. 2021;26:2334\u201349.","journal-title":"Mol Psychiatry"},{"key":"12271_CR38","doi-asserted-by":"publisher","DOI":"10.3390\/genes15040425","volume":"15","author":"E Davydova","year":"2024","unstructured":"Davydova E, Perenkov A, Vedunova M. Building minimized epigenetic clock by iPlex MassARRAY platform. Genes. 2024;15:425.","journal-title":"Genes"},{"key":"12271_CR39","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0108087","volume":"9","author":"D A\u00efssi","year":"2014","unstructured":"A\u00efssi D, Dennis J, Ladouceur M, Truong V, Zwingerman N, Rocanin-Arjo A, et al. Genome-wide investigation of DNA methylation marks associated with FV Leiden mutation. PLoS One. 2014;9:e108087.","journal-title":"PLoS One"},{"key":"12271_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13148-016-0228-z","volume":"8","author":"L Perna","year":"2016","unstructured":"Perna L, Zhang Y, Mons U, Holleczek B, Saum K-U, Brenner H. Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort. Clin Epigenetics. 2016;8:1\u20137.","journal-title":"Clin Epigenetics"},{"key":"12271_CR41","unstructured":"Website. https:\/\/www.genecards.org\/cgi-bin\/carddisp.pl?gene=MAP3K5."},{"key":"12271_CR42","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1089\/rej.2012.1324","volume":"15","author":"AA Johnson","year":"2012","unstructured":"Johnson AA, Akman K, Calimport SRG, Wuttke D, Stolzing A, de Magalh\u00e3es JP. The role of DNA methylation in aging, rejuvenation, and age-related disease. Rejuvenation Res. 2012;15:483\u201394.","journal-title":"Rejuvenation Res"},{"key":"12271_CR43","doi-asserted-by":"publisher","DOI":"10.1186\/s13148-020-00868-8","volume":"12","author":"JV Sandoval-Sierra","year":"2020","unstructured":"Sandoval-Sierra JV, Salgado Garc\u00eda FI, Brooks JH, Derefinko KJ, Mozhui K. Effect of short-term prescription opioids on DNA methylation of the OPRM1 promoter. Clin Epigenetics. 2020;12:76.","journal-title":"Clin Epigenetics"},{"key":"12271_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.drugalcdep.2021.108658","volume":"221","author":"C Shu","year":"2021","unstructured":"Shu C, Sosnowski DW, Tao R, Deep-Soboslay A, Kleinman JE, Hyde TM, et al. Epigenome-wide study of brain DNA methylation following acute opioid intoxication. Drug Alcohol Depend. 2021;221:108658.","journal-title":"Drug Alcohol Depend"},{"key":"12271_CR45","doi-asserted-by":"publisher","DOI":"10.3390\/ijms22126377","volume":"22","author":"O El-Maarri","year":"2021","unstructured":"El-Maarri O, Jamil MA, K\u00f6ster M, N\u00fcsgen N, Oldenburg J, Montag M, et al. Stratifying cumulus cell samples based on molecular profiling to help resolve biomarker discrepancies and to predict oocyte developmental competence. Int J Mol Sci. 2021;22:6377.","journal-title":"Int J Mol Sci"},{"key":"12271_CR46","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-015-0225-4","volume":"7","author":"S Voisin","year":"2015","unstructured":"Voisin S, Alm\u00e9n MS, Zheleznyakova GY, Lundberg L, Zarei S, Castillo S, et al. Many obesity-associated SNPs strongly associate with DNA methylation changes at proximal promoters and enhancers. Genome Med. 2015;7:103.","journal-title":"Genome Med"},{"key":"12271_CR47","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1080\/15592294.2019.1634975","volume":"14","author":"N Fernandez-Jimenez","year":"2019","unstructured":"Fernandez-Jimenez N, Allard C, Bouchard L, Perron P, Bustamante M, Bilbao JR, et al. Comparison of Illumina 450K and EPIC arrays in placental DNA methylation. Epigenetics. 2019;14:1177\u201382.","journal-title":"Epigenetics"},{"key":"12271_CR48","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-016-1066-1","volume":"17","author":"R Pidsley","year":"2016","unstructured":"Pidsley R, Zotenko E, Peters TJ, Lawrence MG, Risbridger GP, Molloy P, et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016;17:208.","journal-title":"Genome Biol"},{"key":"12271_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/gb-2014-15-2-r24","volume":"15","author":"CI Weidner","year":"2014","unstructured":"Weidner CI, Lin Q, Koch CM, Eisele L, Beier F, Ziegler P, et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014;15:1\u201312.","journal-title":"Genome Biol"},{"key":"12271_CR50","doi-asserted-by":"publisher","first-page":"6459","DOI":"10.18632\/aging.202783","volume":"13","author":"A Wo\u017aniak","year":"2021","unstructured":"Wo\u017aniak A, Heidegger A, Piniewska-R\u00f3g D, Po\u015bpiech E, Xavier C, Pisarek A, et al. Development of the VISAGE enhanced tool and statistical models for epigenetic age estimation in blood, buccal cells and bones. Aging. 2021;13:6459\u201384.","journal-title":"Aging"}],"container-title":["BMC Genomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12864-025-12271-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12864-025-12271-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12864-025-12271-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T09:01:55Z","timestamp":1765184515000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcgenomics.biomedcentral.com\/articles\/10.1186\/s12864-025-12271-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,6]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["12271"],"URL":"https:\/\/doi.org\/10.1186\/s12864-025-12271-9","relation":{},"ISSN":["1471-2164"],"issn-type":[{"value":"1471-2164","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,6]]},"assertion":[{"value":"27 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1088"}}