Stefan Bekiranov

This study integrates CAD GWAS with transcriptomic data to identify genes and networks associated with CAD and subclinical atherosclerosis.

This study identifies CEBPD as a tumor suppressor gene in acute myeloid leukemia through functional experiments and genomic analyses.

This study shows that dietary glutamine restriction reduces atherosclerotic plaque rupture mortality by promoting fibrotic smooth muscle cell phenotypes in mice.

DeepGSEA provides an explainable deep learning approach for gene set enrichment analysis in single-cell RNA sequencing data, improving sensitivity and specificity over traditional methods.

The paper introduces a mouse model of advanced atherosclerosis that exhibits spontaneous plaque rupture leading to MI or stroke, and shows that MPO inhibition with AZM198 reduces mortality and plaque rupture incidence.

This study develops a variational autoencoder to generate synthetic scRNA-seq data from bulk RNA-seq, enabling single-cell insights from large-scale bulk datasets.

DARDN uses deep learning to classify cancer-specific CTCF binding sites and identify oncogenic transcription factors in multiple cancer types.

COCOA analyzes epigenetic variation by examining covariation across individuals and annotates heterogeneity using genomic region sets.

This paper identifies a Sir2-regulated locus control region in the recombination enhancer of yeast that controls chromosome III structure and mating-type switching.

The paper investigates CNV neuron frequency in aged human neocortex, finding that it varies widely among individuals and is anti-correlated with age.