Jianzhao Gao

2001/09-2005/06  Minzu University of China, information and computing science, Beijing,Bachelor of Science

2005/09-2010/07  Nankai University, Bioinformatics, Tianjin, Ph.D

2016/12-Now        Nankai University  Associate Professor

2010/07-2016/12  Nankai University  Lecture

Position: Students who aspire to pursue an academic career, have a good command of English, and master at least one programming language (Python, R, Julia, Java, C++, Perl, Matlab, etc.) are welcome to contact me via email: gaojz#AT#nankai.edu.cn

Bioinformatics

Introduction to Data Science

Probability and Statistics for Liberal Arts

Research Projects (Principal Investigator) 

1. General Program of the National Natural Science Foundation of China (NSFC), No. 12571532, Mathematical models of disease-associated cellular heterogeneity based on multi-omics data, 2026/01–2029/12, Principal Investigator (PI).  

2. General Program of the Natural Science Foundation of Tianjin, No. 24JCYBJC01570, Research on structure evaluation of biological macromolecular complexes, 2024/10–2027/09, Principal Investigator (PI).  

3. Young Scientists Program of the Natural Science Foundation of Tianjin, No. 18JCQNJC09600, Research on protein model assessment based on deep learning, 2018/04–2021/03, Principal Investigator (PI).  

4. Young Scientists Program of the National Natural Science Foundation of China (NSFC), No. 11701296, Novel methods for protein structure model evaluation based on structural features and machine learning, 2018/01–2020/12, Principal Investigator (PI).  

5. Research Fund for the Doctoral Program of Higher Education of China (RFDP), No. 20130031120001, Research on several mathematical problems related to B-cell epitopes, 2014/01–2016/12, Principal Investigator (PI).  

 Research Projects (Participant) 

1. Key Interdisciplinary Program of the Mathematical Tianyuan Foundation of NSFC, No. 12426303, Research on screening, optimization and off-target effects of antibody drugs combining high-dimensional statistics and artificial intelligence, 2025/01–2026/12, Participant.  

2. Tianjin Health Science and Technology Project, No. MS20015, Research on intelligent diagnosis of acute large-vessel occlusion cerebral infarction based on MRI, 2020/08–2023/07, Participant.  

1. Xie, W.#, Guo, Y.#, Qi, H., Yang, N., Xiong, L., Gao, J.*, & Zhou, S.*  (2025) Prediction, Design, Synthesis, Insecticidal Activities of Polysubstituted Pyridine Anthranilic Amide Derivatives. Journal of Molecular Structure, 1321. https://doi.org/10.1016/j.molstruc.2024.140203

2. Chen Y# Lin S# Yang S# Qi M, Ren Y, Tian C, Wang S, Yang Y, Gao J*, Zhao H.* (2025) Genetic and phenotypic associations of frailty with cardiovascular indicators and behavioral characteristics. J Adv Res. 71, 263-277. doi: 10.1016/j.jare.2024.06.012

3. Wuyun Q* Chen Y, Shen Y, Cao Y*,Hu G*,Cui W*,Gao J*, Zheng W*, (2024). Recent Progress of Protein Tertiary Structure Prediction. Molecules. 29(4):832. doi: 10.3390/molecules29040832

4. Zeng, Y., Wei, Z., Yuan, Q., Chen, S., Yu, W., Lu, Y., Gao, J.*, & Yang, Y.* (2023). Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model. Bioinformatics, 39(4). https://doi.org/10.1093/bioinformatics/btad187

5. Bai, H., Zhang, Q., Zhang, S., Wang, J., Luo, B., Dong, Y., Gao, J., Cheng, T., Dong, F., & Ema, H. (2022). Multiple cells of origin in common with various types of mouse N-Myc acute leukemia. Leuk Res, 117, 106843. https://doi.org/10.1016/j.leukres.2022.106843

6. Gao, J*., Zheng, S., Yao, M., & Wu, P. (2021). Precise estimation of residue relative solvent accessible area from Cα atom distance matrix using a deep learning method. Bioinformatics, 38(1), 94-98. https://doi.org/10.1093/bioinformatics/btab616

7. Hu, G., Katuwawala, A., Wang, K., Wu, Z., Ghadermarzi, S., Gao, J., & Kurgan, L. (2021). flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions. Nat Commun, 12(1), 4438. https://doi.org/10.1038/s41467-021-24773-7

8. Jin, C., Gao, J., Shi, Z., & Zhang, H. (2021). ATTCry: Attention-based neural network model for protein crystallization prediction. Neurocomputing, 463, 265-274.

9. Necci, M., Piovesan, D., CAID Predictors （including GaoJ.), C. P. I. G., Curators, D., & Tosatto, S. C. E. (2021). Critical assessment of protein intrinsic disorder prediction. Nat Methods, 18(5), 472-481. https://doi.org/10.1038/s41592-021-01117-3

10. Ni, X., Geng, B., Zheng, H., Shi, J., Hu, G., & Gao, J.* (2021). Accurate Estimation of Single-Cell Differentiation Potency Based on Network Topology and Gene Ontology Information. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(6), 3255-3262.

11. Wang, J., Chen, X., Hu, H., Yao, M., Song, Y., Yang, A., Xu, X., Zhang, N., Gao, J.*, & Liu, B.* (2021). PCAT-1 facilitates breast cancer progression via binding to RACK1 and enhancing oxygen-independent stability of HIF-1α. Mol Ther Nucleic Acids, 24, 310-324. https://doi.org/10.1016/j.omtn.2021.02.034

12. Wei, H., Wang, B., Yang, J., & Gao, J.* (2021). RNA flexibility prediction with sequence profile and predicted solvent accessibility. IEEE/ACM Trans Comput Biol Bioinform, 18(5), 2017-2022. https://doi.org/10.1109/tcbb.2019.2956496

13. Ye, L., Wu, P., Peng, Z., Gao, J., Liu, J., & Yang, J. (2021). Improved estimation of model quality using predicted inter-residue distance. Bioinformatics, 37(21), 3752-3759. https://doi.org/10.1093/bioinformatics/btab632

14. Gao, J., Wei, H., Cano, A., & Kurgan, L. (2020). PSIONplus(m) Server for Accurate Multi-Label Prediction of Ion Channels and Their Types. Biomolecules, 10(6). https://doi.org/10.3390/biom10060876

15. Mi, P., Zhang, Q.-P., Zhang, S.-H., Wang, C., Zhang, S.-Z., Fang, Y.-C., Gao, J., Feng, D.-F., Chen, D.-Y., & Feng, X.-Z. (2019). The effects of fluorene-9-bisphenol on female zebrafish (Danio rerio) reproductive and exploratory behaviors. Chemosphere, 228, 398-411. https://doi.org/10.1016/j.chemosphere.2019.04.170

16. Zhang, Z., Ruan, J., Gao, J.*, & Wu, F.-X.* (2019). Predicting essential proteins from protein-protein interactions using order statistics. J Theor Biol, 480, 274-283. https://doi.org/10.1016/j.jtbi.2019.06.022

17. Gao, J., Miao, Z., Zhang, Z., Wei, H., & Kurgan, L. (2019). Prediction of Ion Channels and their Types from Protein Sequences: Comprehensive Review and Comparative Assessment. Curr Drug Targets, 20(5), 579-592. https://doi.org/10.2174/1389450119666181022153942

18. Gao, J., Wu, Z., Hu, G., Wang, K., Song, J., Joachimiak, A., & Kurgan, L. (2018). Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures. Curr Protein Pept Sci, 19(2), 200-210. https://doi.org/10.2174/1389203718666170921114437

19. Gao, J., Yang, Y., & Zhou, Y. (2018). Grid-based prediction of torsion angle probabilities of protein backbone and its application to discrimination of protein intrinsic disorder regions and selection of model structures. BMC Bioinformatics, 19(1), 29. https://doi.org/10.1186/s12859-018-2031-7

20. Yang, Y., Gao, J., Wang, J., Heffernan, R., Hanson, J., Paliwal, K., & Zhou, Y. (2018). Sixty-five years of the long march in protein secondary structure prediction: the final stretch? Brief Bioinform, 19(3), 482-494. https://doi.org/10.1093/bib/bbw129

21. Gao, J., Tao, X.-W., Zhao, J., Feng, Y.-M., Cai, Y.-D., & Zhang, N. (2017). Computational Prediction of Protein Epsilon Lysine Acetylation Sites Based on a Feature Selection Method. Comb Chem High Throughput Screen, 20(7), 629-637. https://doi.org/10.2174/1386207320666170314093216

22. Wang, T., Zheng, W., Wuyun, Q., Wu, Z., Ruan, J., Hu, G., & Gao, J.*(2017). PrAS: Prediction of amidation sites using multiple feature extraction. Comput Biol Chem, 66, 57-62. https://doi.org/10.1016/j.compbiolchem.2016.11.004

23. Gao, J., Yang, Y., & Zhou, Y. (2016). Predicting the errors of predicted local backbone angles and non-local solvent- accessibilities of proteins by deep neural networks. Bioinformatics, 32(24), 3768-3773. https://doi.org/10.1093/bioinformatics/btw549

24. Gao, J., Cui, W., Sheng, Y., Ruan, J., & Kurgan, L. (2016). PSIONplus: Accurate Sequence-Based Predictor of Ion Channels and Their Types. PLoS One, 11(4), e0152964. https://doi.org/10.1371/journal.pone.0152964

25. Zheng, W., Ruan, J., Hu, G., Wang, K., Hanlon, M., & Gao, J.* (2015). Analysis of Conformational B-Cell Epitopes in the Antibody-Antigen Complex Using the Depth Function and the Convex Hull. PLoS One, 10(8), e0134835. https://doi.org/10.1371/journal.pone.0134835

26. Zheng, W., Zhang, C., Hanlon, M., Ruan, J., & Gao, J.* (2014). An ensemble method for prediction of conformational B-cell epitopes from antigen sequences. Comput Biol Chem, 49, 51-58. https://doi.org/10.1016/j.compbiolchem.2014.02.002

27. Gao, J., Hu, G., Wu, Z., Ruan, J., Shen, S., Hanlon, M., & Wang, K. (2014). Improved prediction of protein crystallization, purification and production propensity using hybrid sequence representation. Current Bioinformatics, 9(1), 57-64.

28. Gao, J., & Kurgan, L. (2014). Computational prediction of B cell epitopes from antigen sequences. Methods Mol Biol, 1184, 197-215. https://doi.org/10.1007/978-1-4939-1115-8_11

29. Wang, K., Gao, J., Shen, S., Tuszynski, J. A., Ruan, J., & Hu, G. (2013). An accurate method for prediction of protein-ligand binding site on protein surface using SVM and statistical depth function. Biomed Res Int, 2013, 409658. https://doi.org/10.1155/2013/409658

30. Gao, J., Zhang, N., & Ruan, J. (2013). Prediction of protein modification sites of gamma-carboxylation using position specific scoring matrices based evolutionary information. Comput Biol Chem, 47, 215-220. https://doi.org/10.1016/j.compbiolchem.2013.09.002

31. Zhang, H., Zhang, T., Gao, J., Ruan, J., Shen, S., & Kurgan, L. (2012). Determination of protein folding kinetic types using sequence and predicted secondary structure and solvent accessibility. Amino Acids, 42(1), 271-283. https://doi.org/10.1007/s00726-010-0805-y

32. Gao, J., Faraggi, E., Zhou, Y., Ruan, J., & Kurgan, L. (2012). BEST: improved prediction of B-cell epitopes from antigen sequences. PLoS One, 7(6), e40104. https://doi.org/10.1371/journal.pone.0040104

33. Hu, G., Gao, J., Wang, K., Mizianty, M. J., Ruan, J., & Kurgan, L. (2012). Finding protein targets for small biologically relevant ligands across fold space using inverse ligand binding predictions. Structure, 20(11), 1815-1822. https://doi.org/10.1016/j.str.2012.09.011

34. Wang, K., Cui, W., Hu, G., Gao, J., Wu, Z., Qiu, X., Ruan, J., Feng, Y., Qi, Z., Shao, Y., & Tuszynski, J. A. (2012). Computable features required to evaluate the efficacy of drugs and a universal algorithm to find optimally effective drug in a drug complex. PLoS One, 7(3), e33709. https://doi.org/10.1371/journal.pone.0033709

35. Chen, K., Mizianty, M. J., Gao, J., & Kurgan, L. (2011). A critical comparative assessment of predictions of protein-binding sites for biologically relevant organic compounds. Structure, 19(5), 613-621. https://doi.org/10.1016/j.str.2011.02.015

36. Gao, J., Zhang, T., Zhang, H., Shen, S., Ruan, J., & Kurgan, L. (2010). Accurate prediction of protein folding rates from sequence and sequence-derived residue flexibility and solvent accessibility. Proteins, 78(9), 2114-2130. https://doi.org/10.1002/prot.22727

Sep 02, 2025 – Present　Nanyang Technological University (NTU) 

Sep 12, 2023 – Mar 11, 2024　Chern Institute of Mathematics 

Aug 01, 2017 – Aug 28, 2017　The Chinese University of Hong Kong 

Sep 2015 – May 2016　Griffith University, Australia

Dec. 2016　Selected as a Candidate of the 3rd Level, Tianjin “131” Innovative Talents Training Program