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Unsupervised learning of aging principles from longitudinal data
oleh: Konstantin Avchaciov, Marina P. Antoch, Ekaterina L. Andrianova, Andrei E. Tarkhov, Leonid I. Menshikov, Olga Burmistrova, Andrei V. Gudkov, Peter O. Fedichev
Format: | Article |
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Diterbitkan: | Nature Portfolio 2022-11-01 |
Deskripsi
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.