Kourosh Zarringhalam

This study creates a time-resolved multi-omics map of gene regulation in Toxoplasma gondii by integrating perturbation data with single-cell measurements, revealing regulatory networks and novel interactions.

The paper uses single-cell RNA profiling to show in vivo-derived Toxoplasma bradyzoites undergo two cell cycles, with AP2XI-6 playing a key role in one cycle essential for cyst maturation.

The paper introduces an AI model to analyze mitochondrial morphology in TNBC using TEM images, finding significant structural changes in response to chemotherapy and metastasis.

This study investigates how drug-resistant pancreatic cancer cells alter biophysical interactions with stromal fibroblasts in 3D co-culture models using imaging and RNA-seq analysis.

This paper introduces a Bayesian model to infer transcription factor activity from gene expression data using logical rules and applies it to bulk and single-cell transcriptomics to study fibroblast plasticity.

This paper identifies PhoD as a novel resistance determinant in Babesia species using comparative chemogenomics and validates its role through reverse genetics and functional perturbation.

The paper uses single-cell RNA sequencing to analyze three Babesia species, identifying conserved and species-specific gene expression patterns across the asexual replication cycle.

This paper presents a CNN that predicts HER2 status and trastuzumab response from H&E images with higher accuracy than IHC.

This paper uses CNNs to classify and compare histopathological images across cancer types, revealing conserved spatial patterns and mutation-related features.

ModEx extracts mode of regulation (activation/repression) for TF-gene interactions from ChIP-Seq data using text mining and achieves high F-scores on curated datasets.

The paper introduces CIE, a platform for causal inference of active transcriptional regulators using differential gene expression data and a regularized Gaussian Graphical Model.

The paper introduces Patch-DCA, a method that improves protein interface prediction by integrating sequential, co-evolution, structural, and functional information, achieving significant performance gains over existing methods.