Gustavo Daniel Vega Magdaleno

The paper identifies two gene interaction mechanisms linking ageing and ageing-related diseases using network analysis and machine learning.

The paper assigns stemness scores to human tissues using machine learning and finds a general decline in stemness with age, except in the uterus, and links stemness to proliferation and senescence.

This paper identifies epigenetic loci with age-correlated variance, introduces a deep-learning model to predict age from variance, and proposes a theory of systemic ageing through feedback deregulation.

This paper uses machine learning to classify ageing-related genes as CR-related or not based on various predictive features, finding that curated knowledge features are most predictive.