Nature Communications (Nov 2022)

Unsupervised learning of aging principles from longitudinal data

  • Konstantin Avchaciov,
  • Marina P. Antoch,
  • Ekaterina L. Andrianova,
  • Andrei E. Tarkhov,
  • Leonid I. Menshikov,
  • Olga Burmistrova,
  • Andrei V. Gudkov,
  • Peter O. Fedichev

DOI
https://doi.org/10.1038/s41467-022-34051-9
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 14

Abstract

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Biomarkers of age and frailty may aid in understanding the aging process, predicting lifespan or health span and in assessing the effects of anti-aging interventions. Here, the authors show that combining physics-based models and deep learning may enhance understanding of aging from big biomedical data, observe effects of anti-aging interventions in laboratory animals, and discover signatures of longevity.