Abstract
Predicting chromatin contact maps from 1D epigenomic signals remains a challenging problem in computational biology. Here we present Epiphany, a deep learning method that predicts Hi-C contact maps from 1D epigenomic signals using a novel architecture that combines convolutional and attention mechanisms to capture both local and long-range interactions in chromatin organization.
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.