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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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Biomarkers of aging for the identification and evaluation of longevity interventions
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
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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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