Jie Zhang

ARSA generates evidence-based target shortlists for Alzheimer's disease and related dementias by integrating natural language research interests with molecular data and cross-cohort evidence.

The paper introduces DEGAS, a method to identify high-risk cells in spatial transcriptomics data by transferring disease attributes to individual cells, validated in cancer and diabetes datasets.

The paper introduces DEGASv2, a deep learning framework for analyzing single-cell and spatial transcriptomics data, revealing disease risk heterogeneity and drug response patterns in multiple conditions.

This study examines the proteomic and transcriptomic effects of SHIP1 modulation in microglia, revealing significant proteomic changes in SHIP1 homozygous knockout and differences between BV2 and HMC3 cell lines.

The paper develops a web portal and NLP pipeline to extract and visualize gene-specific Alzheimer's disease publications from PubMed.

This study integrates multi-omic data from brain and blood to identify AD subtypes and proposes LDLR as a blood biomarker linked to brain gene expression changes.

This study examines postmortem brains of AsymAD cases to identify mechanisms of cognitive resilience, finding enhanced microglial dynamics and reduced tau seeding in the amyloid plaque microenvironment.

The paper introduces DEGAS, a transfer learning framework that maps clinical data to spatial transcriptomics in prostate cancer, identifying transcriptional signatures of carcinogenesis in morphologically normal tissues.

This paper introduces SPCS, a two-factor smoothing method for spatial transcriptomics data that improves separability and biological interpretability compared to one-factor methods.

DEGAS transfers disease information from patients to cells using deep transfer learning, identifying PHF19high myeloma cells linked to progression.

The paper develops a CNN model to classify epithelial and stromal tissues in histopathological images and correlates these with gene expression data to identify biological processes.

MORONET is a multi-omics integration method using graph convolutional networks for biomedical classification, outperforming existing approaches and identifying disease-related biomarkers.

TSUNAMI is a tool for mining gene co-expression networks and performing downstream analysis, offering features like web interface, database access, and visualization.

The paper integrates mouse and human single-cell RNA sequencing data to predict and spatially map cell-type composition in human brain tissue.

The paper identifies SMARCA4 and ERK-ETS signaling as targets for suppressing aging-related dopaminergic degeneration in Parkinson's disease using gene co-expression analysis and Drosophila models.

BERMUDA corrects batch effects in scRNA-seq data and reveals hidden cellular subtypes by transferring information across batches.

LAmbDA reduces batch effects and improves subtype detection by integrating datasets with ambiguous labels across species and studies.