Machine learning predicts lifespan and suggests underlying causes of death in aging C. elegans.

ABSTRACT: Aging leads to age-related pathology that causes death, and genes affect lifespan by determining such pathology. Here we investigate how age-related pathology mediates the effect of genetic and environmental interventions on lifespan in C. elegans by means of a data-driven approach employing machine learning (ML). To this end, extensive data on how diverse determinants of lifespan (sex, nutrition, genotype, mean lifespan range: 7.5 to 40 days) affect patterns of age-related pathology was gathered. This revealed that different life-extending treatments result in distinct patterns of suppression of senescent pathology. By analysing the differential effects on mid-life pathology levels and lifespan, the ML models developed were able to predict lifespan variation, explaining 79% of the varianc

SUBMITTER: Kern CC 

PROVIDER: S-EPMC12638908 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature