Qingrun Zhang

The paper introduces scSCOPE, a method for identifying reproducible and functionally relevant marker genes from scRNA-seq data, showing improved consistency across datasets.

This study shows primary cilia modulate astrocyte diversity and function through Shh signaling and intracellular balance, affecting neuronal development and behavior.

OmeSim simulates phenotypes from genotypes and in-between-omes, enabling rigorous benchmarking of multi-omics integration methods.

The paper introduces cLD, a statistic for measuring rare-variant linkage disequilibrium between genomic regions, and shows it identifies novel genomic interactions and associations.

rvTWAS integrates gene expressions with rare variant sequences to identify gene-trait associations in psychiatric disorders.

The paper identifies 58 risk genes associated with common cancers by linking genetically predicted APA levels to cancer risk using GWAS data.

XA4C identifies Critical genes using explainable autoencoders, revealing novel biological insights in cancer gene expression data.

IBAS identifies novel genes associated with complex traits by integrating gene-gene interactions in association studies, showing improved robustness to noise compared to traditional methods.

SCOPE identifies stable core genes and pathways across cancers, revealing shared genome instability pathways and a kidney cancer driver.

The paper introduces XAE4Exp, an explainable autoencoder for gene expression data, identifying non-differentially expressed genes and cancer-related pathways using TCGA data.

This paper shows that combining feature selection and aggregation in GReX limits TWAS power and adaptability, especially with low expression heritability.