Insights to aging prediction with AI based epigenetic clocks.

ABSTRACT: Over the past century, human lifespan has increased remarkably, yet the inevitability of aging persists. The disparity between biological age, which reflects pathological deterioration and disease, and chronological age, indicative of normal aging, has driven prior research focused on identifying mechanisms that could inform interventions to reverse excessive age-related deterioration and reduce morbidity and mortality. DNA methylation has emerged as an important predictor of age, leading to the development of epigenetic clocks that quantify the extent of pathological deterioration beyond what is typically expected for a given age. Machine learning technologies offer promising avenues to enhance our understanding of the biological mechanisms governing aging by further elucidating the gap b

SUBMITTER: Levy JJ 

PROVIDER: S-EPMC11703013 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature