Learning Disentangled Representations of Single Nucleus Epigenomes for Variant Interpretation

Watch this 30-minute conference talk from the Variant to Function (V2F) Symposium at the Broad Institute, where Jesper Madsen, PhD, Associate Professor of Computational Biology at the University of Southern Denmark and researcher at the Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, presents "Learning Disentangled Representations of Single Nucleus Epigenomes for Variant Interpretation." Explore cutting-edge computational approaches to understanding how genetic variants function at the single-cell level through advanced machine learning techniques applied to epigenomic data. Learn about innovative methods for disentangling complex biological signals in single nucleus datasets to better interpret genetic variants and their functional consequences. Discover how these computational biology approaches contribute to the broader challenge of translating genetic associations into actionable therapeutic targets and biomarkers for patient stratification. This presentation is part of a one-day scientific symposium co-chaired by Melina Claussnitzer and Kasper Lage, focusing on accelerating genetically-informed approaches in healthcare through high-impact science and cross-disciplinary discussions between computational biologists, geneticists, and clinical researchers working at the intersection of genomics and precision medicine.