Review Volume 11, Issue 22 pp 10771—10780
Deep biomarkers of aging and longevity: from research to applications
- 1 Insilico Medicine, Hong Kong Science and Technology Park, Hong Kong, China
- 2 The Buck Institute for Research on Aging, Novato, CA 84945, USA
- 3 The Biogerontology Research Foundation, London, UK
- 4 Sinovation Ventures, Beijing, China
- 5 Sinovation AI Institute, Beijing, China
- 6 Deep Longevity, Ltd, Hong Kong Science and Technology Park, Hong Kong, China
received: September 2, 2019 ; accepted: November 8, 2019 ; published: November 25, 2019 ;https://doi.org/10.18632/aging.102475
How to Cite
Copyright © 2019 Zhavoronkov et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Multiple recent advances in machine learning enabled computer systems to exceed human performance in many tasks including voice, text, and speech recognition and complex strategy games. Aging is a complex multifactorial process driven by and resulting in the many minute changes transpiring at every level of the human organism. Deep learning systems trained on the many measurable features changing in time can generalize and learn the many biological processes on the population and individual levels. The deep age predictors can help advance aging research by establishing causal relationships in non-linear systems. Deep aging clocks can be used for identification of novel therapeutic targets, evaluating the efficacy of the various interventions, data quality control, data economics, prediction of health trajectories, mortality, and many other applications. Here we present the current state of development of the deep aging clocks in the context of the pharmaceutical research and development and clinical applications.