Deep biomarkers of aging and longevity: from research to applications.

Abstract:

: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.

journal_name

Aging (Albany NY)

journal_title

Aging

authors

Zhavoronkov A,Li R,Ma C,Mamoshina P

doi

10.18632/aging.102475

subject

Has Abstract

pub_date

2019-11-25 00:00:00

pages

10771-10780

issue

22

issn

1945-4589

pii

102475

journal_volume

11

pub_type

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