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For: Simpson DJ, Chandra T. Epigenetic age prediction. Aging Cell 2021;20:e13452. [PMID: 34415665 DOI: 10.1111/acel.13452] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 10.0] [Reference Citation Analysis]
Number Citing Articles
1 Murach KA, Dimet-Wiley AL, Wen Y, Brightwell CR, Latham CM, Dungan CM, Fry CS, Watowich SJ. Late-life exercise mitigates skeletal muscle epigenetic aging. Aging Cell 2022;21:e13527. [PMID: 34932867 DOI: 10.1111/acel.13527] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Thrush KL, Bennett DA, Gaiteri C, Horvath S, Dyck CHV, Higgins-Chen AT, Levine ME. Aging the brain: multi-region methylation principal component based clock in the context of Alzheimer's disease. Aging (Albany NY) 2022;14. [PMID: 35907208 DOI: 10.18632/aging.204196] [Reference Citation Analysis]
3 Poznyak AV, Sadykhov NK, Kartuesov AG, Borisov EE, Sukhorukov VN, Orekhov AN. Aging of Vascular System Is a Complex Process: The Cornerstone Mechanisms. IJMS 2022;23:6926. [DOI: 10.3390/ijms23136926] [Reference Citation Analysis]
4 Attia MH. A cautionary note on altered pace of aging in the COVID-19 era. Forensic Science International: Genetics 2022;59:102724. [DOI: 10.1016/j.fsigen.2022.102724] [Reference Citation Analysis]
5 Hart DA. Sex Differences in Biological Systems and the Conundrum of Menopause: Potential Commonalities in Post-Menopausal Disease Mechanisms. IJMS 2022;23:4119. [DOI: 10.3390/ijms23084119] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Perez-correa J, Tharmapalan V, Geiger H, Wagner W. Epigenetic Clocks for Mice Based on Age-Associated Regions That are Conserved Between Mouse Strains and Human. Front Cell Dev Biol 2022;10:902857. [DOI: 10.3389/fcell.2022.902857] [Reference Citation Analysis]
7 Di Lena P, Sala C, Nardini C. Evaluation of different computational methods for DNA methylation-based biological age. Brief Bioinform 2022:bbac274. [PMID: 35794713 DOI: 10.1093/bib/bbac274] [Reference Citation Analysis]
8 Douhard F, Douhard M, Gilbert H, Monget P, Gaillard JM, Lemaître JF. How much energetic trade-offs limit selection? Insights from livestock and related laboratory model species. Evol Appl 2021;14:2726-49. [PMID: 34950226 DOI: 10.1111/eva.13320] [Reference Citation Analysis]
9 Hu K, Xu Z, Yao L, Yan Y, Zhou L, Li J. Integrated analysis of expression, prognostic value and immune infiltration of GSDMs in hepatocellular carcinoma. Aging (Albany NY) 2021;13:24117-35. [PMID: 34731088 DOI: 10.18632/aging.203669] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
10 Simpson DJ, Chandra T. Epigenetic age prediction. Aging Cell 2021;20:e13452. [PMID: 34415665 DOI: 10.1111/acel.13452] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 10.0] [Reference Citation Analysis]
11 Niehues A, Bizzarri D, Reinders MJT, Slagboom PE, van Gool AJ, van den Akker EB, 't Hoen PAC; BBMRI-NL BIOS consortium., BBMRI-NL Metabolomics consortium. Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies. BMC Genomics 2022;23:546. [PMID: 35907790 DOI: 10.1186/s12864-022-08771-7] [Reference Citation Analysis]