Pooja Kathail

The paper performs a federated cis-eQTL meta-analysis across single-cell datasets to identify cell-type-specific genetic effects and link them to disease mechanisms.

The paper introduces ember, an entropy-based framework to assess gene specificity in single-cell RNA sequencing data, revealing unexpected patterns in gene expression across tissues and mouse strains.

Pleiades, a whole-genome epigenetic model, detects Alzheimer's and Parkinson's disease from plasma cell-free DNA with high accuracy.

This paper identifies genetic variants in tubule epithelial regulatory elements that explain most heritable differences in human kidney function using GWAS, scATAC-seq, and functional assays.

The paper evaluates deep learning models for predicting chromatin accessibility and finds reduced performance in cell type-specific regions, suggesting strategies to improve accuracy.

The paper introduces MEOTIVE, a statistical framework to identify genetic effects on gene expression variability, revealing new mechanisms of genome regulation and associations with autoimmune disease risk.

This study examines uncertainty in genomic sequence-to-activity model predictions by analyzing consistency across different sequence types and identifies areas where models perform poorly.

This study evaluates genomic deep learning models' ability to explain individual expression variation and finds limited performance.

The paper introduces clue, a reference-free framework for multiplexed single-cell sequencing to identify genetic modifiers of immune responses using SNP-based clustering and eQTL mapping.

MAGIC improves single-cell RNA-seq data by imputing missing values and revealing gene-gene interactions through a diffusion-based method.