Moo Kwon Chung

This paper introduces a fractal-dimension framework to quantify self-similarity in chromatin folding using Hi-C data, revealing fractal-like organization and scale-dependent patterns in chromatin structure.

This paper develops a statistical framework using persistent homology and order statistics to analyze brain networks, finding significant topological differences between male and female brain networks.

This paper introduces a topological learning framework using persistent homology to integrate networks of different sizes and topology, validated on brain imaging data to assess genetic heritability.

The paper introduces a hierarchical structural brain network parcellation method and applies it to twin data to show consistent heritability across network scales.

This study compares structural brain connectivity models using diffusion tensor imaging features in normal controls and maltreated children.

The paper introduces a framework for analyzing paired brain networks using persistent homology and sparse learning, demonstrating its application to twin fMRI data to assess heritability significance.