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Age and life expectancy clocks based on machine learning analysis of mouse frailty

Authors :
Michael B. Schultz
Alice E. Kane
Sarah J. Mitchell
Michael R. MacArthur
Elisa Warner
David S. Vogel
James R. Mitchell
Susan E. Howlett
Michael S. Bonkowski
David A. Sinclair
Source :
Nature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
Publication Year :
2020
Publisher :
Nature Portfolio, 2020.

Abstract

The discovery of interventions that slow aging could be accelerated by employing non-invasive biometrics that predict biological age or life expectancy. Here the authors use longitudinal frailty data from naturally aging mice to develop two such tools, that are responsive to interventions.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.3a49ccbd63f44865818e20ab50fb5065
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-020-18446-0