Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 6, 8570 (2015).
Lehallier, B. et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat. Med. 25, 1843–1850 (2019).
Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).
Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, 3156 (2013).
Unschuld, P. U. & Tessenow, H. Huang Di Nei Jing Su Wen (University of California Press, 2011).
Chen, W., Xia, X., Huang, Y., Chen, X. & Han, J.-D. J. Bioimaging for quantitative phenotype analysis. Methods 102, 20–25 (2016).
Chen, W. et al. Three-dimensional human facial morphologies as robust aging markers. Cell Res. 25, 574–587 (2015).
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The Hallmarks of Aging. Cell 153, 1194–1217 (2013).
Gao, X. W., Hui, R. & Tian, Z. Classification of CT brain images based on deep learning networks. Computer Methods Programs Biomed. 138, 49–56 (2017).
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
Millstein, J., Zhang, B., Zhu, J. & Schadt, E. E. Disentangling molecular relationships with a causal inference test. BMC Genet. 10, 23 (2009).
Debrabant, B. et al. DNA methylation age and perceived age in elderly Danish twins. Mechanisms Ageing Dev. 169, 40–44 (2018).
Lu, T. et al. Gene regulation and DNA damage in the ageing human brain. Nature 429, 883–891 (2004).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Jefferson, A. L. et al. Inflammatory biomarkers are associated with total brain volume. Neurology 68, 1032–1038 (2007).
Frąckiewicz, J. et al. Hematological parameters and all-cause mortality: a prospective study of older people. Aging Clin. Exp. Res. 30, 517–526 (2018).
Chatthanawaree, W. Biomarkers of cobalamin (vitamin B12) deficiency and its application. J. Nutr. Health Aging 15, 227–231 (2011).
Conigrave, K. M., Davies, P., Haber, P. & Whitfield, J. B. Traditional markers of excessive alcohol use. Addiction 98, 31–43 (2003).
Liu, Y. et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat. Biotechnol. 31, 142–147 (2013).
Ahmed, Z. et al. Accelerated lipofuscinosis and ubiquitination in granulin knockout mice suggest a role for progranulin in successful aging. Am. J. Pathol. 177, 311–324 (2010).
He, Z., Ong, C. H. P., Halper, J. & Bateman, A. Progranulin is a mediator of the wound response. Nat. Med. 9, 225–229 (2003).
Elkabets, M. et al. Human tumors instigate granulin-expressing hematopoietic cells that promote malignancy by activating stromal fibroblasts in mice. J. Clin. Invest 121, 784–799 (2011).
Chitramuthu, B. P., Bennett, H. P. J. & Bateman, A. Progranulin: a new avenue towards the understanding and treatment of neurodegenerative disease. Brain 140, 3081–3104 (2017).
Knupp, D. & Miura, P. CircRNA accumulation: a new hallmark of aging? Mechanisms Ageing Dev. 173, 71–79 (2018).
Ruiz, R. et al. Sterol regulatory element-binding protein-1 (SREBP-1) is required to regulate glycogen synthesis and gluconeogenic gene expression in mouse liver. J. Biol. Chem. 289, 5510–5517 (2014).
Oishi, Y. et al. SREBP1 contributes to resolution of pro-inflammatory TLR4 signaling by reprogramming fatty acid metabolism. Cell Metab. 25, 412–427 (2017).
Li, S. et al. Metabolic phenotypes of response to vaccination in humans. Cell 169, 862–877.e817 (2017).
Schoenborn, N. L. et al. Preferred clinician communication about stopping cancer screening among older US adults: results from a national survey. JAMA Oncol. 4, 1126–1128 (2018).
World Medical Association Inc Declaration of Helsinki. Ethical principles for medical research involving human subjects. J. Indian Med. Assoc. 107, 403–405 (2009).
Guo, J., Mei, X. & Tang, K. Automatic landmark annotation and dense correspondence registration for 3D human facial images. BMC Bioinformatics 14, 232 (2013).
King, D. E. Dlib-ml: a Machine Learning Toolkit J. Mach. Learn. Res. 10, 1755–1758 (2009).
Szegedy, C. et al. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1–9 (2015).
Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at https://https://arxiv.org/abs/1409.1556 (2014).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. in IEEE Conference on Computer Vision and Pattern Recognition 770–778 (2016).
Kingma, D. & Ba, J. Adam: A method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).
Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).
Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).
Zhang, X. O. et al. Diverse alternative back-splicing and alternative splicing landscape of circular RNAs. Genome Res. 26, 1277–1287 (2016).
Wold, S., Sjöström, M. & Eriksson, L. PLS-regression: a basic tool of chemometrics. Chemometrics Intell. Lab. Syst. 58, 109–130 (2001).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Dennis, G. et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 4, P3 (2003).
Bhattacharya, S. et al. ImmPort, toward repurposing of open access immunological assay data for translational and clinical research. Sci. Data 5, 180015 (2018).
Coppé, J.-P., Desprez, P.-Y., Krtolica, A. & Campisi, J. The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu Rev. Pathol. 5, 99–118 (2010).
Millstein, J., Chen, G. K. & Breton, C. V. cit: hypothesis testing software for mediation analysis in genomic applications. Bioinformatics 32, 2364–2365 (2016).
