个人简介

张庭赫，华南理工大学未来技术学院助理教授，美国德克萨斯大学圣安东尼奥分校电气与计算机工程博士，宾夕法尼亚大学医学院博士后。主要研究方向为虚拟细胞与RNA甲基化，长期致力于深度学习与机器学习在癌症研究、RNA甲基化及医学图像分析中的应用研究。在Briefings in Bioinformatics、Cancers、PLoS Pathogens等国际期刊发表论文二十余篇，谷歌学术引用700余次，H-index 12。曾受邀在IEEE Biomedical & Health Informatics、International Conference on Intelligent Biology and Medicine等国际学术会议作学术报告。欢迎联系：zhangth@scut.edu.cn

教育背景

2018年–2022年，美国德克萨斯大学圣安东尼奥分校，电气与计算机工程，工学博士

2015年–2017年，美国德克萨斯大学圣安东尼奥分校，电气与计算机工程，工学硕士

2010年–2014年，西北工业大学，自动化学院，工学学士

工作经历

2025年–至今，华南理工大学 未来技术学院，助理教授

2023年–2025年，匹兹堡大学医学院 UPMC 希尔曼癌症中心，博士后研究员  

研究方向

深度学习与机器学习在癌症研究及 RNA 甲基化中的应用

虚拟细胞

标志性成果

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.