Kristina Perevoshchikova

This paper introduces dLEM, a differentiable loop extrusion model that predicts genome folding mechanisms from contact maps and integrates with deep learning to infer extrusion parameters from genomic features.

The paper identifies two DNAm aging signatures in mice, one exponential and one linear, showing differential responses to longevity interventions like caloric restriction and parabiosis.