Diego Borges-Rivera

Mechanistic Genome Folding at Scale through the Differentiable Loop Extrusion Model

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.

Hierarchical Bayesian modeling identifies key considerations in the development of quantitative loop-mediated isothermal amplification assays

The paper develops hierarchical Bayesian models to analyze LAMP amplification data and identifies factors affecting assay resolution using clinical samples and synthetic data.