Delaram Pouyabahar

HLiCA: An integrated cell atlas of the healthy human liver

The paper integrates multiple datasets to create a comprehensive human liver cell atlas, identifying new cell types and associations with demographic factors.

FLASH-MM: fast and scalable single-cell differential expression analysis using linear mixed-effects models

FLASH-MM improves single-cell differential expression analysis by using a fast linear mixed-effects model that is accurate and efficient.

Interpretable single-cell factor decomposition using sciRED

sciRED improves the interpretation of scRNA-seq factor analysis by removing confounding effects and mapping factors to known covariates, revealing biological signals in multiple datasets.

Single-cell transcriptome analysis reveals CD34 as a novel marker of human sinoatrial node pacemaker cardiomyocytes

Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning

This study introduces ExoGRU, a machine learning model that predicts small RNA secretion from sequence, identifies key RNA-binding proteins, and develops exoCLIP to map RNA interactomes in extracellular vesicles.

Single cell profiling reveals strain-specific differences in myeloid inflammatory potential in the rat liver

The paper uses single-cell transcriptomics to compare strain-specific differences in myeloid inflammatory potential in healthy rat livers, revealing strain-specific variations in inflammatory signaling pathways.