Lorenzo Boninsegna

The paper improves binding affinity predictions by using synthetic data, showing that combining synthetic and experimental data enhances model performance.

The paper integrates 4D Nucleome data to analyze 3D genome structure and function in human cells, revealing chromatin domains, loops, and interactions, and showing how genetic variants affect genome folding.

The paper introduces a dimensionality reduction method to classify single-cell chromosome structures into conformational clusters, revealing that distinct morphologies influence gene function through subnuclear positioning.

The paper uses population-based modeling of Hi-C data to analyze the nuclear microenvironment's role in gene function, revealing structural variability and functional heterogeneity.