Biography
Dr. Tinghe Zhang is an Assistant Professor at the School of Future Technology, South China University of Technology. He received his Ph.D. in Electrical and Computer Engineering from the University of Texas at San Antonio, and then worked as a Postdoctoral Fellow at the Perelman School of Medicine, University of Pennsylvania.
His primary research interests include virtual cells and RNA methylation. He has long been dedicated to applying deep learning and machine learning techniques to cancer research, RNA methylation analysis, and medical image analysis. Dr. Zhang has published more than 20 papers in international journals such as Briefings in Bioinformatics, Cancers, and PLoS Pathogens. He has been invited to give academic presentations at international conferences including IEEE Biomedical & Health Informatics and the International Conference on Intelligent Biology and Medicine.
Contact: zhangth@scut.edu.cn
Education
Ph.D. in Electrical and Computer Engineering, University of Texas at San Antonio, USA (2018-2022)
M.S. in Electrical and Computer Engineering, University of Texas at San Antonio, USA (2015-2017)
B.Eng. in Automation, School of Automation, Northwestern Polytechnical University, China (2010-2014)
Work Experience
Assistant Professor, School of Future Technology, South China University of Technology (2023-Present)
Postdoctoral Research Fellow, UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine (2023-2025)
Research Interests
Applications of deep learning and machine learning in cancer research and RNA methylation
Virtual cells
Selected Publications
1.Zhang, T., Jo, S., Zhang, M., Wang, K., Gao, S.-J., Huang, Y. (2024). Understanding YTHDF2-mediated mRNA degradation by m6A-BERT-Deg. Briefings in Bioinformatics, 25(3): bbae170 ,
2.Zhang, T., Hasib, M. M., Chiu, Y. C., Han, Z. F., Jin, Y. F., Flores, M., Chen, Y., Huang, Y. (2022). Transformer for Gene Expression Modeling (T-GEM): An Interpretable Deep Learning Model for Gene Expression-Based Phenotype Predictions. Cancers, 14(19), 1-19.
3.Zhang, T., Flores, M., & Huang, Y. (2021). ES-ARCNN: Predicting enhancer strength by using data augmentation and residual convolutional neural network. Analytical Biochemistry, 618(August 2020), 114120.
4.Zhang, T., & Zhang, S.-W. (2018). Advances in the Prediction of Protein Subcellular Locations with Machine Learning. Current Bioinformatics, 14(5), 406–421.
5.Flores, M., Liu, Z., Zhang, T., Hasib, M. M., Chiu, Y. C., Ye, Z., Paniagua, K., Jo, S., Zhang, J., Gao, S. J., Jin, Y. F., Chen, Y., & Huang, Y. (2022). Deep learning tackles single-cell analysis—a survey of deep learning for scRNA-seq analysis. Briefings in Bioinformatics, 23(1), 1–31.
6.Gruffaz, M., Zhang, T., Marshall, V., Gonçalves, P., Ramaswami, R., Labo, N., Whitby, D., Uldrick, T. S., Yarchoan, R., Huang, Y., & Gao, S. J. (2020). Signatures of oral microbiome in HIV-infected individuals with oral Kaposi’s sarcoma and cell-associated KSHV DNA. PLoS Pathogens, 16(1), 1–18.
7.Chiu, Y.-C., Chen, H.-I. H., Zhang, T., Zhang, S., Gorthi, A., Wang, L.-J., Huang, Y., & Chen, Y. (2019). Predicting drug response of tumors from integrated genomic profiles by deep neural networks. BMC Medical Genomics, 12(S1), 18.
8.Chen, H. I. H., Chiu, Y. C., Zhang, T., Zhang, S., Huang, Y., & Chen, Y. (2018). GSAE: An autoencoder with embedded gene-set nodes for genomics functional characterization. BMC Systems Biology, 12(Suppl 8).
9.Xiao, P., Zhang, T., Huang, Y., & Wang, X. (2024). A Novel QCT-Based Deep Transfer Learning Approach for Predicting Stiffness Tensor of Trabecular Bone Cubes. Irbm, 45(2), 100831.
10.Xiao, P., Zhang, T., Haque, E., Wahlen, T., Dong, X. N., Huang, Y., & Wang, X. (2021). Prediction of Elastic Behavior of Human Trabecular Bone Using A DXA Image-Based Deep Learning Model. Jom, 73(8), 2366–2376.
11.Xiao, P., Zhang, T., Dong, X. N., Han, Y., Huang, Y., & Wang, X. (2020). Prediction of trabecular bone architectural features by deep learning models using simulated DXA images. Bone Reports, 13(July), 100295.
12.Xiao, P., Haque, E., Zhang, T., Dong, X. N., Huang, Y., & Wang, X. (2021). Can DXA image-based deep learning model predict the anisotropic elastic behavior of trabecular bone? Journal of the Mechanical Behavior of Biomedical Materials, 124(April), 104834.
