Maureen Pittman

The paper develops a machine-learning model to predict how structural variants disrupt 3D chromatin structure in congenital heart disease and validates its predictions using induced pluripotent stem cells.

This study introduces novel methods for comparing chromatin contact maps and evaluates their performance using Hi-C data and in silico maps, highlighting the need for biologically informed approaches.

The paper introduces a method to detect gene combinations contributing to congenital heart defects and their comorbidities by analyzing exome sequencing data from families.

The paper uses deep mutational scanning in yeast to show that a toxic conformation of α-synuclein is stable under chemical perturbations, while mutations affecting toxicity are influenced by these perturbations.