Heming Zhang

This paper introduces GiG, a graph AI framework that integrates and interprets whole-person medical and omics data to prioritize biomarkers and improve disease diagnosis.

CellTosg2Sequence integrates biomedical text and signaling graphs into a language model for single-cell analysis, improving performance over existing models.

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.

M3NetFlow is a multi-scale multi-hop graph model for multi-omic data analysis that ranks targets and infers signaling pathways, achieving high accuracy in drug response and Alzheimer's disease studies.

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