Cited by 192Genetics, Aging, and Longevity in Model OrganismsTelomeres, Telomerase, and SenescenceEpigenetics and DNA Methylation

Biohorology and biomarkers of aging: Current state-of-the-art, challenges and opportunities

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Galkin, Fedor, Mamoshina, Polina, Aliper, Alex, de Magalhães, João Pedro, Gladyshev, Vadim N., Zhavoronkov, Alex·2020·Ageing Research Reviews

Abstract

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, termed ageing clocks, are one framework to interpret omics data in the context of ageing and have shown considerable promise.

    “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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  1. Putin, E., Mamoshina, P., Aliper, A., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C., Vijg, J., Zhavoronkov, A., 2017. Deep biomarkers of human aging: application of deep neural networks to biomarker development. Aging (Albany NY) 8, 1021-1033. https://doi.org/10.18632/aging.100968. · DOI

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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
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
  • Health Sciences › Medicine › Physiology › 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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