Juexin Wang

Tracel improves cell segmentation in spatial transcriptomics by assigning transcripts to cells using optimal transport, enhancing cell-type separation and reducing contamination.

This paper introduces STORM, a statistical framework for analyzing spatial patterns in multi-omics data, demonstrating reliable performance in detecting spatial structures and supporting study design decisions.

This study tests the therapeutic effect of 4-phenylbutyrate (PBA) on GABRA1 variants associated with developmental epileptic encephalopathies using cell and mouse models, showing improved receptor expression and function.

This paper demonstrates that the SLC6A1 A305V variant causes GAT-1 destabilization and ER retention, which can be rescued by pharmacologic chaperones like PBA and gene augmentation.

The paper predicts neuronal subtype-specific metabolic changes in neurodegenerative and neuropsychiatric diseases using a systems biology approach and integrates metabolic networks with single nucleus RNA sequencing data.

FUSION is a web-based tool that integrates spatial -omics data with histology to analyze tissue states and identify spatial linkages between molecular and histopathologic features.

TrimNN identifies spatial cell organization patterns from spatial transcriptomics and proteomics data to reveal associations between cell-type arrangements and phenotypes.

This study compares clustering algorithms for cell type annotation in scRNA-seq data, finding that Infomap outperforms others in identifying cell types in PBMC and ROSMAP datasets.

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

scBSP identifies spatially variable genes from spatial transcriptomic data efficiently and accurately across different sequencing techniques and resolutions.

CellDiffusion improves scRNA-seq data integration by accurately inferring cell-cell associations and correcting batch effects across diverse datasets.

This paper introduces REGNN, a graph deep learning framework for analyzing spatially resolved transcriptomics data, showing improved performance in identifying mosaic-like tissue architectures in kidney diseases.

This paper introduces TrimNN, a neural network to estimate network motifs in spatial cell graphs, identifying significant motifs in Alzheimer's disease mouse models.

This paper introduces BSP, a method for identifying spatially variable genes from 2D or 3D spatial transcriptomics data, showing improved accuracy and efficiency in simulations and biological validations.

The paper uses a neural relational inference model to learn long-range interactions in proteins from molecular dynamics simulations, revealing allosteric communication pathways in Pin1, SOD1, and MEK1 systems.

RESEPT uses deep learning to visualize and characterize tissue architecture from spatial transcriptomics data, revealing pathological structures in Alzheimer's and glioblastoma samples.

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

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.

This paper introduces GRGNN, a graph neural network for gene regulatory network inference using supervised and semi-supervised learning, achieving state-of-the-art performance on DREAM5 benchmarks.