Cankun Wang

This study shows that human cardiomyocytes with trisomy 21 are more susceptible to SARS-CoV-2 infection and show an amplified immune response compared to euploid controls.

The paper introduces two methods to profile functional glycan-lectin interactions in tissues using single-cell and spatial multiomic approaches, revealing stimulus-specific glycan remodeling and spatial glycan programs in immune cells and follicular lymphoma.

This paper introduces BioGuider, a multi-agent platform that evaluates and improves bioinformatics software documentation, showing a link between documentation quality and citation frequency.

This paper shows spinal cord injury causes hematopoietic stem cells to enter a repressed state due to glucocorticoid surges, leading to long-term hematopoietic failure, which can be reversed by early glucocorticoid receptor blockade.

This paper introduces DeepSAS, a deep graph framework to identify senescent cells and their associated genes, revealing cell-type-specific senescence features in aging and disease.

The paper develops SenSet, a gene signature for human lung senescence, validated in 3D culture models and identifies cell-type specific senescence mechanisms in aging and disease.

scGNN+ integrates ChatGPT with graph neural networks to improve code optimization, tutorial generation, and visualization in single-cell research.

STREAM infers enhancer-driven gene regulatory networks from single-cell RNA-seq and ATAC-seq data, showing improved performance in TF recovery and enhancer-gene discovery.

ssREAD is a database that compiles single-cell and spatial RNA-seq data for Alzheimer's Disease, offering analysis tools for integrative studies.

TESA improves TFBS identification in ChIP-exo data by integrating nucleotide-resolution signal with a two-stage alignment framework, showing higher accuracy in E. coli datasets.

The paper introduces MEGA, a tool to identify cancer-associated microbes using RNA-seq data from the ORIEN dataset, leveraging graph attention networks and microbial community information.

CEMIG predicts cis-regulatory motifs from ATAC-seq data, outperforming existing methods and identifying cell-type-specific and shared TF motifs in GM12878 and K562 cells.

This study uses spatial transcriptomics to identify gene signatures in the human temporal cortex linked to Alzheimer's pathology.

This study shows that aging of CNS-resident cells worsens EAE severity and reduces remission in mice, suggesting microglial activation and aging properties as therapeutic targets for progressive MS.

The paper uses spatial transcriptomics and the MAPLE algorithm to analyze fibrotic lung tissue, identifying spatial biomarkers and fibrotic programs in IPF.

DeepMAPS infers biological networks from single-cell multi-omics data using a heterogeneous graph transformer, outperforming existing tools in cell clustering and pathway prediction.

This study evaluates 20 deep learning models for cis-regulatory motif finding using genomic data, finding that model choice depends on data type and size.

scGNN introduces a graph neural network framework for scRNA-Seq analyses, improving gene imputation and cell clustering on benchmark datasets and revealing disease-related neural development in Alzheimer's disease.

scREAD is a database that integrates and provides analysis of scRNA-Seq and snRNA-Seq datasets from Alzheimer's disease human and mouse models.

The paper introduces DESSO, a deep learning framework for predicting regulatory motifs from ChIP-Seq data, showing improved accuracy over existing tools.

IRIS-DGE is a server-based tool for differential gene expression analysis that integrates multiple R-based methods and provides interactive visualizations for RNA-seq data.

GeneQC estimates the reliability of gene expression levels from RNA-Seq data by analyzing mapping uncertainty, improving downstream analysis validity.