Abstract
Single-cell chromatin accessibility profiling has emerged as a powerful approach to understand cellular heterogeneity and gene regulatory mechanisms. However, predicting 3D chromatin contact maps from single-cell accessibility data remains challenging. Here we present ChromaFold, a deep learning method that predicts 3D contact maps from single-cell chromatin accessibility data, enabling the study of chromatin organization dynamics across different cell types and states.
Alireza Karbalayghareh
Senior Research Scientist, AI/ML
My research focuses on developing universal AI models of gene regulation in immune cells, capturing cis-regulation, trans-regulation, and intercellular signaling by using single-cell multiomic observational, spatial, time-resolved, and perturbational data across diverse biological contexts to understand, engineer, and reprogram the immune system to prevent and treat diseases.