Nour El Kazwini

TopOmics introduces a flexible topic modeling framework for multi-omics data, demonstrating competitive performance and interpretability across diverse datasets.

CRAK-Velo improves RNA velocity estimation by integrating chromatin accessibility data, enabling accurate cell-type deconvolution and insights into gene-chromatin interactions.

SHARE-Topic is a Bayesian model for multi-omic single-cell data that identifies interpretable patterns of co-variation between gene expression and epigenomic layers, demonstrating its utility in B-cell lymphoma analysis.

NeuroVelo reconstructs cellular dynamics and identifies gene regulatory networks from single-cell transcriptomic data using dynamical systems theory.