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

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.

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Autores principales: 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
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Acceso en línea:https://doaj.org/article/3a49ccbd63f44865818e20ab50fb5065
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spelling oai:doaj.org-article:3a49ccbd63f44865818e20ab50fb50652021-12-02T18:34:02ZAge and life expectancy clocks based on machine learning analysis of mouse frailty10.1038/s41467-020-18446-02041-1723https://doaj.org/article/3a49ccbd63f44865818e20ab50fb50652020-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18446-0https://doaj.org/toc/2041-1723The 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.Michael B. SchultzAlice E. KaneSarah J. MitchellMichael R. MacArthurElisa WarnerDavid S. VogelJames R. MitchellSusan E. HowlettMichael S. BonkowskiDavid A. SinclairNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
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
Age and life expectancy clocks based on machine learning analysis of mouse frailty
description 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.
format article
author 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
author_facet 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
author_sort Michael B. Schultz
title Age and life expectancy clocks based on machine learning analysis of mouse frailty
title_short Age and life expectancy clocks based on machine learning analysis of mouse frailty
title_full Age and life expectancy clocks based on machine learning analysis of mouse frailty
title_fullStr Age and life expectancy clocks based on machine learning analysis of mouse frailty
title_full_unstemmed Age and life expectancy clocks based on machine learning analysis of mouse frailty
title_sort age and life expectancy clocks based on machine learning analysis of mouse frailty
publisher Nature Portfolio
publishDate 2020
url https://doaj.org/article/3a49ccbd63f44865818e20ab50fb5065
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AT davidasinclair ageandlifeexpectancyclocksbasedonmachinelearninganalysisofmousefrailty
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