Guangfu Li

BioMedGraphica integrates biomedical knowledge into a unified graph and introduces a Textual-Numeric Graph structure for multi-omics analysis.

OmniCellAgent autonomously retrieves and analyzes scRNA-seq datasets to generate structured reports and hypotheses across disease settings.

This study introduces mosGraphFlow, a graph AI model for analyzing multi-omic data to identify AD-associated biomarkers and signaling pathways, achieving high classification accuracy.

mosGraphGPT is a foundation model for multi-omic signaling graphs trained on cancer data and fine-tuned for Alzheimer's Disease, improving classification accuracy and interpretability.

PathFinder infers multi-cell signaling pathways and communications using a graph transformer model, identifying novel interactions in AD mouse models.

mosGraphGen generates multi-omics signaling graphs to enable integrative and interpretable graph AI model development by converting multi-omics data into biologically meaningful graphs.

This study identifies GAS5 as a regulator of TGF-β-induced fibroblast activation by inhibiting Smad3 signaling, showing therapeutic potential for fibrotic diseases.