L. I. Men’shikov

This paper develops a deep learning model to identify a blood-based biomarker of aging in mice using phenotypic data, finding it correlates with aging hallmarks and predicts lifespan.

The paper analyzes longitudinal blood marker data to show aging is associated with declining physiological resilience and predicts a lifespan limit around 120-150 years.

The paper identifies a universal transcriptomic signature of aging in C. elegans and shows it can predict lifespan extension and drug effects.

The paper quantifies biological age and frailty using locomotor activity data from NHANES and UKB, linking aging trajectories to mortality and health outcomes.

This study compares biological age models using NHANES data, finding that supervised models improve age estimation but reduce mortality prediction accuracy, and introduces a new mortality risk model.

This paper shows that the Strehler-Mildvan correlation arises from fitting degeneracy in Gompertz models, not from biological homogeneity, and demonstrates this through analysis of C. elegans survival data.