Junwen Wang

This paper introduces CLG, a structural-functional calibration method for volumetric calcium imaging that corrects neuron identity errors, reducing duplicate counts and improving network analysis accuracy.

The paper introduces ceQTL, a method to detect eQTLs that affect transcription factor binding and target gene regulation using differential co-expression analysis.

Immunopipe provides a flexible pipeline for integrated analysis of scRNA-seq and scTCR-seq data to enhance understanding of T-cell immunity and immunotherapy.

This paper introduces MSF-CPMP, a multi-source feature fusion model for predicting cyclic peptide membrane permeability, achieving high accuracy and AUROC scores.

The paper develops DeepAnchor, a deep learning model to characterize CTCF binding patterns and predict CTCF-anchored loops across human tissues, revealing sequence and chromatin features that influence loop formation and disease mechanisms.

This paper introduces a federated model to integrate sMRI, GWAS, and transcriptomics data in Alzheimer's disease to identify genetic and transcriptomic influences on brain imaging measures.

This study introduces a computational method to classify cancer driver genes as oncogenes or tumor suppressors based on mutation patterns across 33 cancer types, identifying novel genes and revealing context-specific functions.

MAGOS improves subclone detection in tumors sequenced at standard depths by correcting sequencing errors and outperforms existing methods in accuracy and efficiency.

The paper analyzes mRNA data from 17 cancer types to identify dysregulated pathways, finding Pathways in Cancer as the most common and MAPK signaling as the most influential.

The paper integrates DNA methylation and transcriptomic data from Cysticercus cellulosae to show DNA methylation regulates key parasitism genes.

The paper identifies genetic variations affecting ceRNA regulation by analyzing RNA-seq data and GWAS results, revealing new regulatory mechanisms and disease associations.