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Biohorology and biomarkers of aging: Current state-of-the-art, challenges and opportunities
Open accessAbstract
The aging process results in multiple traceable footprints, which can be quantified and used to estimate an organism's age. Examples of such aging biomarkers include epigenetic changes, telomere attrition, and alterations in gene expression and metabolite concentrations. More than a dozen aging clocks use molecular features to predict an organism's age, each of them utilizing different data types and training procedures. Here, we offer a detailed comparison of existing mouse and human aging clocks, discuss their technological limitations and the underlying machine learning algorithms. We also discuss promising future directions of research in biohorology - the science of measuring the passage of time in living systems. Overall, we expect deep learning, deep neural networks and generative approaches to be the next power tools in this timely and actively developing field.
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“Chronological age predictors, which the field has colloquially termed ageing clocks43,44, are one framework to interpret omics data in the context of ageing and has shown considerable promise45.”
From Measuring biological age using omics data (2022) · cites this paper as [45]
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Cites
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
DNA methylation age of human tissues and cell types
Horvath, S. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r115 · Cited by 7,075
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682
Adversarial Discriminative Domain Adaptation
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.316 · Cited by 5,154
Pleiotropy, natural selection, and the evolution of senescence.
Williams, G. C. (1957) · Evolution · doi:10.1111/j.1558-5646.1957.tb02911.x · Cited by 4,279
Genome-wide methylation profiles reveal quantitative views of human aging rates
Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.016 · Cited by 4,270
An epigenetic biomarker of aging for lifespan and healthspan
Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., Hou, L., Baccarelli, A. A., Stewart, J. D., Li, Y., et al. (2018) · Aging · doi:10.18632/aging.101414 · Cited by 3,675
CpG islands and the regulation of transcription
Deaton, A. M., & Bird, A. (2011) · Genes & Development · doi:10.1101/gad.2037511 · Cited by 3,187
DNA methylation-based biomarkers and the epigenetic clock theory of ageing
Horvath, S., & Raj, K. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0004-3 · Cited by 3,095
Permutation importance: a corrected feature importance measure
Altmann, A., Toloşi, L., Sander, O., & Lengauer, T. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq134 · Cited by 3,051
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A. A., & Darrell, T. (2017) · arXiv · doi:10.48550/arxiv.1711.03213 · Cited by 2,656
DNA methylation GrimAge strongly predicts lifespan and healthspan
Lu, A. T., Quach, A., Wilson, J. G., Reiner, A. P., Aviv, A., Raj, K., Hou, L., Baccarelli, A. A., Li, Y., Stewart, J. D., et al. (2019) · Aging · doi:10.18632/aging.101684 · Cited by 2,540
Generative Adversarial Text to Image Synthesis
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016) · arXiv · doi:10.48550/arxiv.1605.05396 · Cited by 2,494
Pleiotropy, natural selection, and the evolution of senescence
Williams, G. C. (1957) · Evolution · doi:10.2307/2406060 · Cited by 1,912
Association between telomere length in blood and mortality in people aged 60 years or older
Cawthon, R. M., Smith, K. R., O'Brien, E., Sivatchenko, A., & Kerber, R. A. (2003) · The Lancet · doi:10.1016/s0140-6736(03)12384-7 · Cited by 1,761
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573
Self-citationJylhävä, J., Pedersen, N. L., & Hägg, S. (2017) · EBioMedicine · doi:10.1016/j.ebiom.2017.03.046 · Cited by 1,240
Histone Deacetylase Inhibitors as Anticancer Drugs
Eckschlager, T., Plch, J., Stiborova, M., & Hrabeta, J. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18071414 · Cited by 1,206
Domain-adversarial training of neural networks
GaninYaroslav, UstinovaEvgeniya, AjakanHana, GermainPascal, LarochelleHugo, LavioletteFrançois, MarchandMario, & LempitskyVictor (2016) · Journal of Machine Learning Research · doi:10.5555/2946645.2946704 · Cited by 1,203
Bocklandt, S., Lin, W., Sehl, M. E., Sánchez, F. J., Sinsheimer, J. S., Horvath, S., & Vilain, E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0014821 · Cited by 1,012
Leucocyte telomere length and risk of cardiovascular disease: systematic review and meta-analysis
Haycock, P. C., Heydon, E. E., Kaptoge, S., Butterworth, A. S., Thompson, A., & Willeit, P. (2014) · BMJ · doi:10.1136/bmj.g4227 · Cited by 856
Aging of blood can be tracked by DNA methylation changes at just three CpG sites
Weidner, C. I., Lin, Q., Koch, C. M., Eisele, L., Beier, F., Ziegler, P., Bauerschlag, D. O., Jöckel, K.-H., Erbel, R., Mühleisen, T. W., et al. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r24 · Cited by 837
Aging-Associated Cardiovascular Changes and Their Relationship to Heart Failure
Strait, J. B., & Lakatta, E. G. (2011) · Heart Failure Clinics · doi:10.1016/j.hfc.2011.08.011 · Cited by 769
Liu, Z., Kuo, P.-L., Horvath, S., Crimmins, E., Ferrucci, L., & Levine, M. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002718 · Cited by 754
Meta-analysis of age-related gene expression profiles identifies common signatures of aging
de Magalhães, J. P., Curado, J., & Church, G. M. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp073 · Cited by 737
The transcriptional landscape of age in human peripheral blood
Peters, M. J., Joehanes, R., Pilling, L. C., Schurmann, C., Conneely, K. N., Powell, J., Reinmaa, E., Sutphin, G. L., Zhernakova, A., Schramm, K., et al. (2015) · Nature Communications · doi:10.1038/ncomms9570 · Cited by 725
Adult lean body mass declines with age: Some longitudinal observations
Forbes, G. B., & Reina, J. C. (1970) · Metabolism · doi:10.1016/0026-0495(70)90062-4 · Cited by 684
Advances and Applications of Single-Cell Sequencing Technologies
Wang, Y., & Navin, N. E. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.05.005 · Cited by 624
Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., & Zhavoronkov, A. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00346 · Cited by 587
Self-citationTelomere and telomerase in stem cells
Hiyama, E., & Hiyama, K. (2007) · British Journal of Cancer · doi:10.1038/sj.bjc.6603671 · Cited by 573
Telomere Length in Epidemiology: A Biomarker of Aging, Age-Related Disease, Both, or Neither?
Sanders, J. L., & Newman, A. B. (2013) · Epidemiologic Reviews · doi:10.1093/epirev/mxs008 · Cited by 556
Marioni, R. E., Shah, S. H., McRae, A. F., Ritchie, S. J., Terrera, G. M., Harris, S. E., Gibson, J., Redmond, P. H., Cox, S. R., Pattie, A., et al. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyu277 · Cited by 549
Adversarial Discriminative Domain AdaptationPreprint
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.05464 · Cited by 515
Causal Interpretations of Black-Box Models
Zhao, Q., & Hastie, T. J. (2019) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2019.1624293 · Cited by 490
Role of Oxidative Stress in Telomere Length Regulation and Replicative Senescence
von Zglinicki, T. (2000) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2000.tb06639.x · Cited by 452
Belsky, D. W., Moffitt, T. E., Cohen, A. A., Corcoran, D. L., Levine, M. E., Prinz, J. A., Schaefer, J. D., Sugden, K., Williams, B. S., Poulton, R., et al. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx346 · Cited by 436
An Attempt to Prevent Senescence: A Mitochondrial Approach
Skulachev, V. P., Anisimov, V. N., Antonenko, Y. N., Bakeeva, L. E., Chernyak, B. V., Erichev, V. P., Filenko, O. F., Kalinina, N. I., Kapelko, V. I., Kolosova, N. G., et al. (2009) · Biochimica et Biophysica Acta (BBA) - Bioenergetics · doi:10.1016/j.bbabio.2008.12.008 · Cited by 430
Human naive and memory T lymphocytes differ in telomeric length and replicative potential.
Weng, N., Levine, B. L., June, C. H., & Hodes, R. J. (1995) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.92.24.11091 · Cited by 418
Reduced histone biosynthesis and chromatin changes arising from a damage signal at telomeres
O'Sullivan, R. J., Kubicek, S., Schreiber, S. L., & Karlseder, J. (2010) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1897 · Cited by 416
Personal aging markers and ageotypes revealed by deep longitudinal profiling
Ahadi, S., Zhou, W., Schüssler-Fiorenza Rose, S. M., Sailani, M. R., Contrepois, K., Avina, M., Ashland, M., Brunet, A., & Snyder, M. (2020) · Nature Medicine · doi:10.1038/s41591-019-0719-5 · Cited by 408
The Role of DNA Methylation in Aging, Rejuvenation, and Age-Related Disease
Johnson, A. A., Akman, K., Calimport, S., Wuttke, D., Stolzing, A., & de Magalhães, J. P. (2012) · Rejuvenation Research · doi:10.1089/rej.2012.1324 · Cited by 399
Using DNA methylation profiling to evaluate biological age and longevity interventions
Petkovich, D. A., Podolskiy, D. I., Lobanov, A. V., Lee, S.-G., Miller, R. A., & Gladyshev, V. N. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.03.016 · Cited by 398
Self-citationReinforced Adversarial Neural Computer for de Novo Molecular Design
Lane, E., Asadulaev, A., Ivanenkov, Y. A., Aladinskiy, V., Sánchez-Lengeling, B., Aspuru‐Guzik, A., & Zhavoronkov, A. A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00690 · Cited by 388
Self-citationMulti-tissue DNA methylation age predictor in mouse
BI Ageing Clock Team, Stubbs, T. M., Bonder, M. J., Stark, A.-K., Krueger, F., von Meyenn, F., Stegle, O., & Reik, W. (2017) · Genome Biology · doi:10.1186/s13059-017-1203-5 · Cited by 384
Haendeler, J., Hoffmann, J., Diehl, J. F., Vasa‐Nicotera, M., Spyridopoulos, I., Zeiher, A. M., & Dimmeler, S. (2004) · Circulation Research · doi:10.1161/01.res.0000121104.05977.f3 · Cited by 382
Physical Exercise Prevents Cellular Senescence in Circulating Leukocytes and in the Vessel Wall
Werner, C., Fürster, T., Widmann, T., Pöss, J., Roggia, C., Hanhoun, M., Scharhag, J., Büchner, N., Meyer, T., Kindermann, W., et al. (2009) · Circulation · doi:10.1161/circulationaha.109.861005 · Cited by 380
Deep biomarkers of human aging: Application of deep neural networks to biomarker development
Putin, E., Mamoshina, P., Aliper, A. M., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C. R., Vijg, J., & Zhavoronkov, A. A. (2016) · Aging · doi:10.18632/aging.100968 · Cited by 378
Self-citationTranscriptome signature of cellular senescence
Casella, G., Munk, R., Kim, K. M., Piao, Y., De, S., Abdelmohsen, K., & Gorospe, M. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz555 · Cited by 359
Does oxidative stress shorten telomeres in vivo ? A review
Reichert, S., & Stier, A. (2017) · Biology Letters · doi:10.1098/rsbl.2017.0463 · Cited by 324
Single-cell chromatin modification profiling reveals increased epigenetic variations with aging
Cheung, P., Vallania, F., Warsinske, H. C., Donato, M., Schaffert, S., Chang, S. E., Dvorak, M., Dekker, C. L., Davis, M. M., Utz, P. J., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.079 · Cited by 323
Martin-Ruiz, C. M., Gussekloo, J., van Heemst, D., von Zglinicki, T., & Westendorp, R. G. J. (2005) · Aging Cell · doi:10.1111/j.1474-9726.2005.00171.x · Cited by 322
Stress, cell senescence and organismal ageing
de Magalhães, J. P., & Passos, J. F. (2018) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2017.07.001 · Cited by 316
Comparison of telomere length measurement methods
Lai, T., Wright, W. E., & Shay, J. W. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2016.0451 · Cited by 301
Wang, T., Tsui, B., Kreisberg, J. F., Robertson, N., Gross, A. M., Yu, M., Carter, H. K., Brown‐Borg, H. M., Adams, P. D., & Ideker, T. (2017) · Genome biology · doi:10.1186/s13059-017-1186-2 · Cited by 296
Gladyshev, V. N. (2016) · Aging Cell · doi:10.1111/acel.12480 · Cited by 268
Self-citationThe ageing ovary and uterus: new biological insights
Nelson, S. M., Telfer, E. E., & Anderson, R. A. (2012) · Human Reproduction Update · doi:10.1093/humupd/dms043 · Cited by 262
Recent results: Biomarkers of aging
Johnson, T. E. (2006) · Experimental Gerontology · doi:10.1016/j.exger.2006.09.006 · Cited by 257
From Source to Target and Back: Symmetric Bi-Directional Adaptive GAN
Russo, P., Carlucci, F. M., Tommasi, T., & Barbara Caputo, B. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00845 · Cited by 249
DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy
Lin, Q., Weidner, C. I., Costa, I. G., Marioni, R. E., Ferreira, M. R. P., Deary, I. J., & Wagner, W. (2016) · Aging · doi:10.18632/aging.100908 · Cited by 247
Visualizing the Effects of Predictor Variables in Black Box Supervised Learning ModelsPreprint
Apley, D. W., & Zhu, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.08468 · Cited by 247
Assessment of Biological Age Using a Profile of Physical Parameters
Borkan, G. A., & Norris, A. H. (1980) · Journal of Gerontology · doi:10.1093/geronj/35.2.177 · Cited by 239
Epigenetic Rejuvenation of Mesenchymal Stromal Cells Derived from Induced Pluripotent Stem Cells
Frobel, J., Hemeda, H., Lenz, M., Abagnale, G., Joussen, S., Denecke, B., Šarić, T., Zenke, M., & Wagner, W. (2014) · Stem Cell Reports · doi:10.1016/j.stemcr.2014.07.003 · Cited by 232
Predicting age from the transcriptome of human dermal fibroblasts
Fleischer, J. G., Schulte, R., Tsai, H. H., Tyagi, S., Ibarra, A., Shokhirev, M. N., Huang, L., Hetzer, M. W., & Navlakha, S. (2018) · Genome Biology · doi:10.1186/s13059-018-1599-6 · Cited by 232
Mamoshina, P., Volosnikova, M., Ozerov, I. V., Lane, E., Skibina, E., Cortese, F., & Zhavoronkov, A. A. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00242 · Cited by 231
Self-citationDeep Feature Selection: Theory and Application to Identify Enhancers and Promoters
Li, Y., Chen, C., & Wasserman, W. W. (2016) · Journal of Computational Biology · doi:10.1089/cmb.2015.0189 · Cited by 215
Chromatin structure as a mediator of aging
Feser, J., & Tyler, J. K. (2010) · FEBS Letters · doi:10.1016/j.febslet.2010.11.016 · Cited by 206
Aging: The Reality: Biomarkers of Aging: From Primitive Organisms to Humans
Butler, R. N., Sprott, R., Warner, H. R., Bland, J. S., Feuers, R. J., Forster, M. J., Fillit, H. M., Harman, S. M., Hewitt, M., Hyman, M., et al. (2004) · The Journals of Gerontology Series A · doi:10.1093/gerona/59.6.b560 · Cited by 205
A multi-tissue full lifespan epigenetic clock for mice
Thompson, M. J., Chwiałkowska, K., Rubbi, L., Lusis, A. J., Davis, R. C., Srivastava, A., Korstanje, R., Churchill, G. A., Horvath, S., & Pellegrini, M. (2018) · Aging · doi:10.18632/aging.101590 · Cited by 203
Evolutionary Theories of Aging and Longevity
Gavrilov, L. A., & Gavrilova, N. S. (2002) · The Scientific World JOURNAL · doi:10.1100/tsw.2002.96 · Cited by 201
Mamoshina, P., Kochetov, K., Lane, E., Cortese, F., Aliper, A. M., Lee, W., Ahn, S., Lee, U., Skjodt, N. M., Kovalchuk, O., et al. (2018) · The Journals of Gerontology Series A · doi:10.1093/gerona/gly005 · Cited by 200
Self-citationColuzzi, E., Colamartino, M., Cozzi, R., Leone, S., Meneghini, C., O’Callaghan, N., & Sgura, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0110963 · Cited by 197
Molecular and phenotypic biomarkers of aging
Xia, X., Chen, W., McDermott, J., & Han, J.-D. J. (2017) · F1000Research · doi:10.12688/f1000research.10692.1 · Cited by 188
Detection of Senescence-Associated Heterochromatin Foci (SAHF)
Aird, K. M., & Zhang, R. (2012) · Methods in molecular biology · doi:10.1007/978-1-62703-239-1_12 · Cited by 185
Cole, J. J., Robertson, N. A., Rather, M. I., Thomson, J. P., McBryan, T., Sproul, D., Wang, T., Brock, C., Clark, W., Ideker, T., et al. (2017) · Genome Biology · doi:10.1186/s13059-017-1185-3 · Cited by 182
Telomere Shortening in Neural Stem Cells Disrupts Neuronal Differentiation and Neuritogenesis
Ferrón, S. R., Marqués-Torrejón, M. Á., Mira, H., Flores, I., Taylor, K., Blasco, M. A., & Fariñas, I. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.3836-09.2009 · Cited by 179
A whole lifespan mouse multi-tissue DNA methylation clock
Meer, M. V., Podolskiy, D. I., Tyshkovskiy, A., & Gladyshev, V. N. (2018) · eLife · doi:10.7554/elife.40675 · Cited by 176
Self-citationTelomere attrition as ageing biomarker.
Bekaert, S., Meyer, T. D., & Oostveldt, P. V. (2005) · PubMed · Cited by 158
Chatsirisupachai, K., Palmer, D. H., Ferreira, S., & de Magalhães, J. P. (2019) · Aging Cell · doi:10.1111/acel.13041 · Cited by 152
Programmatic features of aging originating in development: aging mechanisms beyond molecular damage?
de Magalhães, J. P. (2012) · The FASEB Journal · doi:10.1096/fj.12-210872 · Cited by 150
Telomerase activity in ‘immortal’ fish1
Klapper, W., Heidorn, K., Kühne, K., Parwaresch, R. M., & Krupp, G. (1998) · FEBS Letters · doi:10.1016/s0014-5793(98)01020-5 · Cited by 149
CpG island density and its correlations with genomic features in mammalian genomes
Han, L., Su, B., Li, W., & Zhao, Z. (2008) · Genome biology · doi:10.1186/gb-2008-9-5-r79 · Cited by 145
Coevolution of telomerase activity and body mass in mammals: From mice to beavers
Gorbunova, V. A., & Seluanov, A. (2008) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2008.02.008 · Cited by 143
Wang, T., Johnson, T. S., Shao, W., Lu, Z., Helm, B. R., Zhang, J., & Huang, K. (2019) · Genome biology · doi:10.1186/s13059-019-1764-6 · Cited by 143
Telomeres and human reproduction
Kalmbach, K., Antunes, D. M. F., Dracxler, R. C., Knier, T. W., Seth-Smith, M. L., Wang, F., Liu, L., & Keefe, D. L. (2012) · Fertility and Sterility · doi:10.1016/j.fertnstert.2012.11.039 · Cited by 137
Leukocyte telomere dynamics and human hematopoietic stem cell kinetics during somatic growth
Sidorov, I., Kimura, M., Yashin, A., & Aviv, A. (2009) · Experimental Hematology · doi:10.1016/j.exphem.2008.11.009 · Cited by 135
Lin, Y., Damjanovic, A., Metter, E. J., Nguyen, H., Truong, T., Najarro, K., Morris, C., Longo, D. L., Zhan, M., Ferrucci, L., et al. (2015) · Clinical Science · doi:10.1042/cs20140481 · Cited by 132
Blood Cell Telomere Length Is a Dynamic Feature
Svenson, U., Nordfjäll, K., Baird, D., Roger, L., Osterman, P., Hellenius, M.-L., & Roos, G. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021485 · Cited by 132
Longevity of lobsters is linked to ubiquitous telomerase expression
Klapper, W., Kühne, K., Singh, K. K., Heidorn, K., Parwaresch, R. M., & Krupp, G. (1998) · FEBS Letters · doi:10.1016/s0014-5793(98)01357-x · Cited by 129
Assessment of biological age by principal component analysis
Nakamura, E., Miyao, K., & Ozeki, T. (1988) · Mechanisms of Ageing and Development · doi:10.1016/0047-6374(88)90109-1 · Cited by 127
PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging
Bobrov, E., Georgievskaya, A., Kiselev, K., Sevastopolsky, A., Zhavoronkov, A. A., Gurov, S., Владимирович, Р. К., Tobar, M. D. P. B., Jaspers, S., & Clemann, S. (2018) · Aging · doi:10.18632/aging.101629 · Cited by 127
Self-citationTelomere Shortening and Alzheimer’s Disease
Cai, Z., Yan, L., & Ratka, A. (2012) · NeuroMolecular Medicine · doi:10.1007/s12017-012-8207-9 · Cited by 121
The Epidemiology of Human Telomeres: Faults and Promises
Aviv, A. (2008) · The Journals of Gerontology Series A · doi:10.1093/gerona/63.9.979 · Cited by 121
Activity Dependency and Aging in the Regulation of Adult NeurogenesisPreprint
Kempermann, G. (2015) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a018929 · Cited by 120
Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers
Mamoshina, P., Kochetov, K., Cortese, F., Kovalchuk, A., Aliper, A. M., Lane, E., Scheibye‐Knudsen, M., Cantor, C. R., Skjodt, N. M., Kovalchuk, O., et al. (2019) · Scientific Reports · doi:10.1038/s41598-018-35704-w · Cited by 115
Self-citationTowards natural mimetics of metformin and rapamycin
Aliper, A. M., Jellen, L. C., Cortese, F., Artemov, A. V., Karpinsky‐Semper, D., Moskalev, A., Swick, A. G., & Zhavoronkov, A. A. (2017) · Aging · doi:10.18632/aging.101319 · Cited by 112
Self-citationIs There a Relationship between Mean Blood Glucose and Glycated Hemoglobin?
Makris, K., & Spanou, L. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500634 · Cited by 110
Martin-Herranz, D. E., Aref‐Eshghi, E., Bonder, M. J., Stubbs, T. M., Choufani, S., Weksberg, R., Stegle, O., Sadiković, B., Reik, W., & Thornton, J. M. (2019) · Genome biology · doi:10.1186/s13059-019-1753-9 · Cited by 101
Maintained memory in aging is associated with young epigenetic age
Degerman, S., Josefsson, M., Adolfsson, A. N., Wennstedt, S., Landfors, M., Haider, Z., Pudas, S., Hultdin, M., Nyberg, L. H., & Adolfsson, R. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.02.009 · Cited by 96
Smooth Function Approximation by Deep Neural Networks with General Activation Functions
Ohn, I., & Kim, Y. (2019) · Entropy · doi:10.3390/e21070627 · Cited by 93
Aging, exceptional longevity and comparisons of the Hannum and Horvath epigenetic clocks
Armstrong, N. J., Mather, K. A., Thalamuthu, A., Wright, M. J., Trollor, J. N., Ames, D. J., Brodaty, H., Schofield, P. R., Sachdev, P. S., & Kwok, J. B. (2017) · Epigenomics · doi:10.2217/epi-2016-0179 · Cited by 84
Is 60 the New 50? Examining Changes in Biological Age Over the Past Two Decades
Levine, M. E., & Crimmins, E. M. (2018) · Demography · doi:10.1007/s13524-017-0644-5 · Cited by 83
Transcriptome signature of cellular senescence
Casella, G., Munk, R., Kim, K. M., Piao, Y., De, S., Abdelmohsen, K., & Gorospe, M. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz879 · Cited by 83
Changes in nucleosome occupancy associated with metabolic alterations in aged mammalian liver
Bochkis, I. M., Przybylski, D., Chen, J., & Regev, A. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.09.048 · Cited by 78
Telomerase expression in the mammalian heart
Richardson, G. D., Breault, D. T., Horrocks, G., Cormack, S., Hole, N., & Owens, W. A. (2012) · The FASEB Journal · doi:10.1096/fj.12-208843 · Cited by 71
A reassessment of the telomere hypothesis of senescence
Reddel, R. R. (1999) · BioEssays · doi:10.1002/(sici)1521-1878(199812)20:12 · Cited by 65
Epigenetic age-predictor for mice based on three CpG sites
Han, Y., Eipel, M., Franzen, J., Sakk, V., Dethmers-Ausema, B., Yndriago, L., Izeta, A., de Haan, G., Geiger, H., & Wagner, W. (2018) · eLife · doi:10.7554/elife.37462 · Cited by 64
Epigenetic aging clocks in mice and men
Wagner, W. (2017) · Genome biology · doi:10.1186/s13059-017-1245-8 · Cited by 57
Telomere Shortening in Alzheimer's Disease Patients.
Liu, M., Huo, Y. R., Wang, J., Wang, C., Liu, S., Liu, S., Wang, J., & Ji, Y. (2016) · PubMed · Cited by 53
M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
Laradji, I., & Babanezhad, R. (2020) · Springer eBooks · doi:10.1007/978-3-030-30671-7_2 · Cited by 52
Harris, S. E., Riggio, V., Evenden, L., Gilchrist, T. L., McCafferty, S., Murphy, L., Wrobel, N., Taylor, A. M., Corley, J., Pattie, A., et al. (2017) · Aging · doi:10.18632/aging.101333 · Cited by 50
Gialluisi, A., Castelnuovo, A. F. D., Donati, M. B., de Gaetano, G., Iacoviello, L., & the Moli-sani Study Investigators (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00146 · Cited by 50
Genomic landscape of CpG rich elements in human
Babenko, V. N., Чадаева, И. В., & Orlov, Y. L. (2017) · BMC Evolutionary Biology · doi:10.1186/s12862-016-0864-0 · Cited by 48
Changes in chromatin structure during aging of human skin fibroblasts
Ishimi, Y., Kojima, M., Takeuchi, F., Miyamoto, T., Yamada, M.-A., & Hanaoka, F. (1987) · Experimental Cell Research · doi:10.1016/0014-4827(87)90206-0 · Cited by 45
Zhang, Y., Hapala, J., Brenner, H., & Wagner, W. (2017) · Clinical Epigenetics · doi:10.1186/s13148-017-0315-9 · Cited by 45
Biomarkers and ageing: The clock-watcher
Gibbs, W. W. (2014) · Nature · doi:10.1038/508168a · Cited by 42
Telomere biology: Cancer firewall or aging clock?
Mitteldorf, J. (2013) · Biochemistry (Moscow) · doi:10.1134/s0006297913090125 · Cited by 36
Trans-species learning of cellular signaling systems with bimodal deep belief networks
Chen, L., Cai, C., Chen, V., & Lu, X. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv315 · Cited by 31
Using deep learning to associate human genes with age-related diseases
Fabris, F., Palmer, D. H., Salama, K. M., de Magalhães, J. P., & Freitas, A. A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz887 · Cited by 22
Milne, E. M. G. (2006) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2005.11.003 · Cited by 21
Association of facial ageing with DNA methylation and epigenetic age predictions
Marioni, R. E., Belsky, D. W., Deary, I. J., & Wagner, W. (2018) · Clinical Epigenetics · doi:10.1186/s13148-018-0572-2 · Cited by 21
Dolejš, J., & Marešová, P. (2017) · Clinical Interventions in Aging · doi:10.2147/cia.s119327 · Cited by 19
From source to target and back: symmetric bi-directional adaptive GANPreprint
Russo, P., Carlucci, F. M., Tommasi, T., & Barbara Caputo, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08824 · Cited by 10
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · arXiv
Research works printed but not linked: 1
- Putin, 2017, Deep biomarkers of human aging: application of deep neural networks to biomarker development, Aging (Albany NY), 8, 1021, 10.18632/aging.100968 · DOI
Cited by
Sorted by citations
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Moqri, M., Herzog, C., Poganik, J. R., Justice, J., Belsky, D. W., Higgins-Chen, A., Moskalev, A., Fuellen, G., Cohen, A. A., Bautmans, I., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.003 · Cited by 736
Self-citationMeasuring biological age using omics data
Rutledge, J., Oh, H., & Wyss-Coray, T. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00511-7 · Cited by 570
Bao, H., Cao, J., Chen, M., Min, C., Chen, W., Chen, X., Chen, Y., Chen, Y., Chen, Y., Chen, Z., et al. (2023) · Science China Life Sciences · doi:10.1007/s11427-023-2305-0 · Cited by 402
BiT age: A transcriptome‐based aging clock near the theoretical limit of accuracy
Meyer, D. H., & Schumacher, B. (2021) · Aging Cell · doi:10.1111/acel.13320 · Cited by 165
DeepMAge: A Methylation Aging Clock Developed with Deep Learning
Galkin, F., Mamoshina, P., Kochetov, K., Sidorenko, D., & Zhavoronkov, A. A. (2021) · Aging and Disease · doi:10.14336/ad.2020.1202 · Cited by 156
Self-citation
Metadata
- Authors
- Galkin, F., Mamoshina, P., Aliper, A., de Magalhães, J. P., Gladyshev, V. N., & Zhavoronkov, A.
- Affiliations
- University of Liverpool (GB)
- Brigham and Women's Hospital (US)
- Buck Institute for Research on Aging (US)
- Corresponding author
- Zhavoronkov, Alex
- Venue
- Ageing Research Reviews
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 60
- Pages
- 101050
- ISSN
- 1568-1637 (ISSN-L)
- OpenAlex
- W3015295307
- Cited by
- 189
- References
- 123
- Citations by year
- 2026: 15 · 2025: 36 · 2024: 45 · 2023: 40 · 2022: 26 · 2021: 22 · 2020: 5
- FWCI
- 2.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Ageing Research Reviews (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Telomeres, Telomerase, and Senescence
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- aging biomarkers, aging clocks, epigenetic aging, telomere shortening, gene expression, metabolites, machine learning, deep learning, generative models, age estimation
- MeSH terms
- Machine Learning, Aging, Algorithms, Animals, Humans, Biomarkers, Neural Networks, Computer, Mice
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