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.
Guardado en:
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
|
Materias: | |
Acceso en línea: | https://doaj.org/article/3a49ccbd63f44865818e20ab50fb5065 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Publisher Correction: Age and life expectancy clocks based on machine learning analysis of mouse frailty
por: Michael B. Schultz, et al.
Publicado: (2020) -
Animal models of frailty: current applications in clinical research
por: Kane AE, et al.
Publicado: (2016) -
Femtosecond time synchronization of optical clocks off of a flying quadcopter
por: Hugo Bergeron, et al.
Publicado: (2019) -
Machine learning helps identify CHRONO as a circadian clock component.
por: Ron C Anafi, et al.
Publicado: (2014) -
Sermones temáticos sobre escatología y profecía /
por: MacArthur, John
Publicado: (2015)