About

I am a PhD candidate at Washington University School of Medicine (WUSM) focused on Agentic AI, Large Reasoning Models (LRMs), and precision medicine. Working with Dr. Fuhai Li and Dr. Michael Province, I developed a biomedical AI ecosystem for multi-omics integration and analysis; and AI Co-Scientist Agent to enable RL-guided knowledge-grounded reasoning for target/drug hypothesis generation. I also worked with H. Henry Lai, MD on lower urinary tract symptoms (LUTS) outcomes modeling and with Dr. Li Ding on multi-omics integration and analysis for pancreatic ductal adenocarcinoma (PDAC).

I earned my M.S. in Computer Science from Washington University in St. Louis in 2021. During my master’s, I joined Dr. Fuhai Li’s lab at the Institute for Informatics, applying computational and deep learning methods—especially graph neural networks—to analyze cell-signaling interactions and predict drug-combination effects.

I completed my B.S. in Information Systems at Central China Normal University (CCNU) in 2019 as a “Boya” Plan student and was recognized as an “Outstanding Graduate,” focusing my early research on text mining.

In 2018, supported by a Mitacs fellowship, I was a VIRS research scholar at the University of British Columbia (UBC) under the supervision of Dr. Ivan Beschastnikh, working on systems and machine learning.

1. GALAX: Graph-Augmented Language Model for Explainable Reinforcement-Guided Subgraph Reasoning in Precision Medicine
   Heming Zhang, Di Huang, Wenyu Li, Michael Province, Yixin Chen, Philip Payne, Fuhai Li
   arXiv Preprint, September 2025
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2. BioMedGraphica: An All-in-One Platform for Biomedical Prior Knowledge and Omic Signaling Graph Generation
   Heming Zhang*, Shunning Liang*, Tim Xu*, Wenyu Li, Di Huang, Yuhan Dong, et al., Carlos Cruchaga, Yixin Chen, Michael Province, Philip Payne, Fuhai Li
   bioRxiv Preprint, September 2025
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3. OmniCellAgent: Towards AI Co-Scientists for Scientific Discovery in Precision Medicine
   Di Huang*, Hao Li*, Wenyu Li*, Heming Zhang*, et al., Carlos Cruchaga, Michael Province, Yixin Chen, Philip Payne, Fuhai Li
   bioRxiv Preprint, August 2025
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4. OmniCellTOSG: The First Cell Text-Omic Signaling Graphs Dataset for Joint LLM and GNN Modeling
   Heming Zhang*, Tim Xu*, Dekang Cao*, Shunning Liang, Lars Schimmelpfennig, Levi Kaster, Di Huang, Carlos Cruchaga, et al., Philip Payne, Fuhai Li
   arXiv Preprint, April 2025
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5. M3NetFlow: a novel multi-scale multi-hop modular graph AI model for multi-omics data integration and signaling network inference
   Heming Zhang, Peter Goedegebuure, Li Ding, William Hawkins, David DeNardo, Ryan Fields, Yixin Chen, Fuhai Li
   iScience, February 2025
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6. GraphSeqLM: A Unified Graph Language Framework for Omic Graph Learning
   Heming Zhang, Di Huang, Yixin Chen, Fuhai Li
   WWW 2025
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7. mosGraphFlow: a novel integrative graph AI model mining disease targets from multi-omic data
   Heming Zhang, Dekang Cao, Tim Xu, Emily Chen, Guangfu Li, Yixin Chen, Philip Payne, Michael Province, Fuhai Li
   BMC Methods 2025
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8. Using DeepSignalingFlow to mine signaling flows interpreting mechanism of synergy of cocktails
   Heming Zhang, Yixin Chen, Philip R Payne, Fuhai Li
   NPJ Systems Biology and Applications, 2024
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9. mosGraphGen: a novel tool to generate multi-omic signaling graphs to facilitate integrative and interpretable graph AI model development
   Heming Zhang*, Dekang Cao*, Zirui Chen, Ziyuan Zhang, Yixin Chen, Cole Sessions, et al., Guangfu Li, Michael Province, Fuhai Li
   Bioinformatics Advances, 2024
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10. Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling
    Zehao Dong, Heming Zhang, Yixin Chen, Philip R Payne, Fuhai Li
    Cancers, August 2023
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11. Predicting anti-cancer drug response with deep learning constrained by signaling pathways
    Heming Zhang, Yixin Chen, Fuhai Li
    Frontiers in Bioinformatics, 2021
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12. Investigate the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model
    Jiarui Feng, Heming Zhang, Fuhai Li
    BMC Bioinformatics, 2020
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13. Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model
    Heming Zhang, Jiarui Feng, Amanda Zeng, Philip Payne, Fuhai Li
    AMIA 2020 Annual Symposium (Oral Presentation)
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• hemingzhang@wustl.edu
• Washington University in St. Louis