Jiang Bian

Merging CellAge, GenAge, SenMayo and Reactome senescence genes onto Ensembl IDs yields 1,250 provenance-tracked genes, most also annotated with mouse/zebrafish orthologs, tissue expression and STRING interactions.

The paper introduces Corpusome, a harmonized human microbiome corpus spanning multiple body sites, to improve cross-body-site representation and cross-study generalization in microbiome research.

scPyviewer provides a Python-native interactive viewer for AnnData single-cell data, matching R Shiny tools' features and outperforming them in speed and memory efficiency.

GatorPrism integrates spatial multi-omics data by balancing cross-omics consensus with modality-specific signals, achieving strong performance across clustering metrics and revealing tissue structure organization.

GatorDuo improves spatial domain identification in spatial transcriptomics by refining dual graphs with pseudo-label agreement and adaptive clustering.

GatorSC improves single-cell transcriptomic analysis by integrating multi-scale cell and gene graphs with a Mixture-of-Experts fusion framework, outperforming existing methods in clustering, imputation, and annotation.

This study identifies risk factors and patterns of immune-related adverse events (irAEs) in patients receiving immune checkpoint inhibitors using real-world data.

GatorST improves spatial transcriptomic analysis by integrating graph-based modeling, pseudo-labeling, and contrastive meta-learning to enhance spatial domain identification and downstream tasks.

This study identifies hyper-glycosylation as a driver of Alzheimer's disease through metabolic analysis and interventions, showing its impact on cognitive decline and mortality.

SEHI-PPI predicts human-influenza protein-protein interactions using a double-view deep learning framework with adaptive negative sampling, outperforming existing methods and demonstrating strong generalizability.

This paper introduces scSimGCL, a graph contrastive learning framework for scRNA-seq data that improves cell clustering through self-supervised pretraining and high-quality representation learning.

Gra-CRC-miRTar is a pre-trained graph neural network framework for identifying miRNA targets in colorectal cancer, achieving better performance than existing methods and revealing functional interactions.

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

OCTOPUS is a metagenomics classifier for portable sequencing data that improves accuracy and reduces false positives on mobile devices.

The paper identifies miR-320a as a regulator of the unfolded protein response by analyzing TSS-miRNA targets in colorectal cancer cells using AGO-qCLASH.

The paper introduces ODAC, a privacy-preserving distributed algorithm for multi-center Cox proportional hazard models, showing it matches pooled estimates and outperforms meta-analysis for rare events.