Shu Zhang

The paper uses deep learning to analyze Alu elements' impact on genome folding, identifying sequence and genomic context features that influence chromatin interactions and boundary formation.

The paper uses a convolutional neural network to predict chromatin organization disruptions caused by somatic structural variants in pediatric tumors, identifying high-impact SVs and regions associated with cancer.

This study shows that reduced TBX5 dosage affects 3D chromatin organization in human cardiomyocytes, with cell-to-cell variation and partial protection by CTCF co-occupancy.

This paper introduces two methods to detect recurrent genomic rearrangements in cancer genomes and identifies 80 simple and 29 complex rearrangements across 2,500 cancers.

The paper investigates mutation patterns during double-strand break repair in yeast, finding that deletions and tandem duplications occur with specific microhomology preferences and structural constraints.

The paper compares germline and somatic structural variants in cancer, identifying differences in their generating processes and selection, and develops a classifier to distinguish them in tumor samples without matched normals.