高建召

2005年-2010年  南开大学    数学科学学院  生物信息学  理学博士

2001年-2005年  中央民族大学 数学与计算机学院 信息与计算科学系 理学学士

2010年-2016年  南开大学数学科学学院，讲师

2016年-至今   南开大学数学科学学院，副教授

我的研究兴趣包括(但不限于）：(1) 蛋白质结构功能预测（2）单细胞数据分析（3）免疫信息学（4）AI在生物数据中的应用。

生物信息学、机器学习算法，深度学习在生命科学中的应用等。 

常年招收硕士研究生，欢迎英语较好，掌握一门计算机语言(Python, Java,C++,Perl,R,Matlab等)的同学和我联系。

1.公共必修课（A）：

《一元函数微积分》，《多元函数微积分》，《文科概率统计》《文科高等数学》《高等数学（物理类）》习题课，《高等数学（信息类）》习题课，《一元函数微分、积分》

2.数学科学学院专业选修课（D）：《生物信息学》，《信息论》

• 1.天津市自然科学基金、18JCQNJC09600、基于深度学习的蛋白质模型评估研究、2018/04-2021/03、主持。

• 2.国家自然科学基金青年项目、11701296、基于结构特征和机器学习的蛋白质结构模型评估新方法研究、2018/01-2020/12、主持。

• 3.教育部博士点基金、20130031120001、B细胞表位相关若干数学问题研究、2014/01-2016/12、主持。

• 4.中央高校基本科研业务费、65011491、基于序列信息的构象型B细胞表位预测、2011/07-2013/06、主持。

期刊论文：

[1]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

[2]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

[3]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

[4]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

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

[6]Necci, M., Piovesan, D., CAID Predictors （including Gao J.), 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

[7]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.

[8]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

[9]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

[10]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

[11]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

[12]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

[13]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

[14]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

[15]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

[16]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

[17]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

[18]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

[19]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

[20]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

[21]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

[22]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

[23]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

[24]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.

[25]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

[26]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

[27]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

[28]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

[29]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

[30]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

[31]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

[32]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

[33]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 

专著:

[1]沈世镒，胡刚，王奎，高建召，信息动力学与生物信息学-蛋白质与蛋白质组的结构分析，北京：科学出版社，74万字，2011，ISBN：9787030316806。

[2]Gao J, Kurgan L*, Computational prediction of B cell epitopes from antigen sequences (Book chapter), Immunoinformatics (Second Edition), Methods in Molecular Biology (series Editor: John Walker), Humana Press, 2014;1184:197-215. doi: 10.1007/978-1-4939-1115-8_11.

[3]沈世镒，胡刚，王奎，高建召，张拓，蛋白质分析与数学-生物、医学与医院卫生中的定量化研究，北京：科学出版社，94万字，2014，ISBN：9787030408402。

(1)2023年09月12日~ 2024年03月11日陈省身数学研究所

(2)2017年08月01日~ 2017年08月28日到香港中文大学统计系，樊晓丹教授课题组访问 (https://www.sta.cuhk.edu.hk/peoples/xfan/ )。

(3)2015年09月~ 2016年05月到澳大利亚Griffith 大学Institute for Glycomics中心Yaoqi Zhou教授课题组访问（http://zhouyq-lab.szbl.ac.cn/）。

  

l2016年12月，获天津市“131”创新型人才培养工程第三层次人选

l2020年06月，获南开大学青年教师教学竞赛一等奖

l2020年08月，获天津市第十五届高校青年教师教学竞赛三等奖