Zhana Duren

REGA-perturb resolves gene regulatory paths from genetic perturbations using reference epigenomes and RNA measurements, outperforming existing methods in identifying TF-target relationships and predicting regulatory changes.

This paper introduces REGA, a framework that uses reference regulatory elements to infer gene regulatory networks from expression data, showing improved performance across multiple benchmarks and applications.

Epiregulon infers single-cell TF activity using ATAC-seq and RNA-seq to predict drug response and identify drivers of cell state.

The paper uses multiomic profiling of the caudate nucleus to identify gene expression and chromatin changes in AUD, linking them to risk genes through QTL analysis.

LINGER improves GRN inference from single-cell multiome data by integrating atlas-scale bulk data and TF motif knowledge, achieving 2-3 fold higher accuracy.

SpecVar improves heritability enrichment by integrating regulatory networks with GWAS data to identify relevant tissues and shared heritability.

The paper examines dynamic chromatin changes in human CAR T cells during exhaustion, identifying regulatory networks and enhancer roles in exhaustion-associated genes.

This paper develops a Bayesian framework integrating GWAS with regulatory networks to improve the identification of trait-associated genes by modeling network topology.

GuidingNet predicts DNA methyltransferase genome-wide binding by integrating multi-omics data and reveals transcriptional cofactors involved in methylation.

This study shows that tyrosine kinase inhibitors cause mitochondrial dysfunction in cardiomyocytes by altering GATA4-mediated networks, and GATA4 overexpression can rescue these effects.

This study introduces CC-NMF, a framework that jointly infers clonal structure from matched scDNA and scRNA data, showing robustness and accuracy in resolving genomic and transcriptomic clonal architecture.

The paper introduces coupled nonnegative matrix factorizations to integrate single cell RNA-seq and ATAC-seq data by coupling clustering across samples.

The paper identifies TFAP2C and p63 as key regulators of epidermal lineage commitment and shows TFAP2C initiates differentiation while p63 drives maturation through chromatin dynamics.

This paper introduces scABC, an R package for unsupervised clustering of scATAC-seq data to classify cell types based on open chromatin regions.