ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

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

My research interests include AI/ML for biology, gene regulation, single-cell genomics, and using them to engineer immune system for effective cancer therapies.