Ms Tong Pan
Grant-Funded Research Associate
SAIGENCI
College of Health
Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.
Dr Tong Pan is a computational biologist and Postdoctoral Research Fellow at the South Australian Immunogenomics Cancer Institute (SAiGENCI), the University of Adelaide. Her research focuses on developing artificial intelligence and statistical learning methods to interpret high-throughput proteomic and spatial transcriptomic data. Tong completed her PhD in Biochemistry and Molecular Biology at Monash University, where she developed computational frameworks for predicting protein function, identifying catalytic residues, and analysing spatially resolved gene expression. She has extensive experience with large-scale transcriptomic and clinical datasets, Python-based machine learning, and integrative molecular data analysis.
My research focuses on developing machine learning and geometric deep learning methods to understand complex biological systems, particularly in proteomics and spatial transcriptomics. During my PhD at Monash University, I developed biologically grounded and scalable computational frameworks aimed at translating high-throughput molecular data—such as enzyme kinetics, catalytic residues, and spatial gene expression—into actionable biological insights.
I am especially interested in building bio-graph foundation models and advanced AI tools that bridge molecular sequences, structures, and spatial context. My work has appeared in leading journals and conferences, including Nucleic Acids Research, Bioinformatics, Genomics, Proteomics & Bioinformatics, and the AAAI Conference on Artificial Intelligence. Through these efforts, I aim to create robust computational approaches that deepen our understanding of protein function and enhance the analysis of spatially resolved omics data.
| Date | Position | Institution name |
|---|---|---|
| 2025 - 2026 | Postdoc | University of Adelaide |
| Date | Institution name | Country | Title |
|---|---|---|---|
| 2022 - 2025 | Monash University | Australia | PhD |
| Year | Citation |
|---|---|
| 2025 | Liu, C., Cai, S., Pan, T., Ogata, H., Song, J., & Akutsu, T. (2025). SFM-Net: Selective Fusion of Multiway Protein Feature Network for Predicting Binding Affinity Changes upon Mutations. Journal of Chemical Information and Modeling, 65(7), 3854-3865. |
| 2025 | Ran, Z., Guo, X., Pan, T., Bi, Y., Hao, Y., Sun, H., . . . Li, F. (2025). A scalable equivariant graph network framework for precise protein function prediction. Genome Biology, 26(1), 23 pages. Scopus1 Europe PMC1 |
| 2025 | Zhang, Y., Yu, H. J., Yan, Z. H., Pan, T., Zhang, Y., Liu, Y., . . . Yu, D. J. (2025). Integrating Graph Convolutional Networks for Missing Gene Expression Imputation. IEEE Transactions on Computational Biology and Bioinformatics, 22(6), 1-10. |
| 2024 | Pan, T., Bi, Y., Wang, X., Zhang, Y., Webb, G., Gasser, R. B., . . . Song, J. (2024). SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations. Genomics, Proteomics & Bioinformatics, 22(6), qzae094-1-qzae094-14. Scopus2 WoS1 Europe PMC4 |
| 2024 | Bi, Y., Li, F., Wang, C., Pan, T., Davidovich, C., Webb, G. I., & Song, J. (2024). Advancing microRNA target site prediction with transformer and base-pairing patterns.. Nucleic acids research, 52(19), gkae782. Scopus4 WoS6 Europe PMC3 |
| 2023 | Cui, Y., Wang, Z., Wang, X., Zhang, Y., Zhang, Y., Pan, T., . . . Song, J. (2023). SMG: self-supervised masked graph learning for cancer gene identification. Briefings in Bioinformatics, 24(6), bbad406. Scopus14 WoS11 Europe PMC6 |
| 2023 | Wang, Z., Bi, Y., Pan, T., Wang, X., Bain, C., Bassed, R., . . . Song, J. (2023). Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification. Bioinformatics, 39(3), btad114. Scopus19 WoS15 Europe PMC14 |
| 2023 | Pan, T., Li, C., Bi, Y., Wang, Z., Gasser, R. B., Purcell, A. W., . . . Song, J. (2023). PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships. Bioinformatics, 39(3), btad094. Scopus35 WoS33 Europe PMC27 |
| 2022 | Bi, Y., Li, F., Guo, X., Wang, Z., Pan, T., Guo, Y., . . . Song, J. (2022). Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations. Briefings in Bioinformatics, 23(6), bbac467-1-bbac467-12. Scopus24 WoS23 Europe PMC21 |
| 2019 | Pan, T., Xu, J., Jiang, S., & Xu, F. (2019). Cell-like spiking neural P systems with evolution rules. Soft Computing, 23(14), 5401-5409. Scopus9 WoS8 |
| 2018 | Pan, T., Shi, X., Zhang, Z., & Xu, F. (2018). A small universal spiking neural P system with communication on request. Neurocomputing, 275, 1622-1628. Scopus20 WoS18 |
| Year | Citation |
|---|---|
| 2025 | Pan, T., Webb, G. I., Imoto, S., & Song, J. (2025). Integrating Gene Ontology Relationships for Protein Function Prediction Using PFresGO. In Methods in Molecular Biology (Vol. 2947, pp. 161-169). Springer US. DOI |
| Year | Citation |
|---|---|
| 2025 | Han, R., Liu, X., Pan, T., Xu, J., Wang, X., Lan, W., . . . Chen, T. (2025). CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction. In T. Walsh, J. Shah, & Z. Kolter (Eds.), Proceedings of the Aaai Conference on Artificial Intelligence Vol. 39 (pp. 246-254). PA, Philadelphia: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. DOI Scopus4 WoS3 |
| Year | Citation |
|---|---|
| 2025 | Pan, T., Cui, X., Koh, H. Y., Bi, Y., Wang, X., Zhang, Y., . . . Song, J. (2025). KcatNet: A Geometric Deep Learning Framework for Genome- Wide Prediction of Enzyme Catalytic Efficiency. DOI |
| 2025 | Ran, Z., Guo, X., Pan, T., Bi, Y., Hao, Y., Sun, H., . . . Li, F. (2025). ENGINE: A Scalable Equivariant Graph Network Framework for Precise Protein Function Prediction. DOI |
| 2025 | Pan, T., Cui, X., Koh, H. Y., Bi, Y., Wang, X., Zhang, Y., . . . Song, J. (2025). KcatNet: advancing genome-wide enzyme <i>K</i><sub><i>cat</i></sub>prediction through structural enzymatic characterization. DOI Europe PMC1 |
| 2025 | Wang, X., Pan, T., Chen, S., Webb, G., Jiang, Y., Rozowsky, J., . . . Song, J. (2025). Predicting Disease-Specific Histone Modifications and Functional Effects of Non-coding Variants by Leveraging DNA Language Models. DOI |
| 2024 | Bi, Y., Li, F., Wang, C., Pan, T., Davidovich, C., Webb, G., & Song, J. (2024). Advancing microRNA Target Site Prediction with Transformer and Base-Pairing Patterns. DOI |
| 2024 | Pan, T., Bi, Y., Wang, X., Zhang, Y., Webb, G., Gasser, R., . . . Song, J. (2024). SCREEN: a graph-based contrastive learning tool to infer catalytic residues and assess mutation tolerance in enzymes. DOI |
| Date | Role | Research Topic | Program | Degree Type | Student Load | Student Name |
|---|---|---|---|---|---|---|
| 2026 | Co-Supervisor | Comprehensively analysing molecular features of metabolic dysfunction-associated steatohepatitis-driven liver cancer | Doctor of Philosophy | Doctorate | Full Time | Miss Jingjing Gao |
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