一、基本信息

张健，博士

副教授，硕士生导师

研究领域：生物信息学、人工智能

邮箱：jianzhang@xynu.edu.cn

网站: http://www.inforstation.com/biocomlab/

二、研究领域

(1) 生物序列计算

(2) 蛋白质固有无序

(3) 蛋白质-药物相互作用

(4) 深度学习与人工智能

三、科研项目

(1) 国家自然科学基金青年基金项目，61802329，高通量核酸、配体、蛋白质结合位点的差异性分析与特异化预测，2019/01-2021/12，已结题，主持

(2) 河南省自然科学基金面上项目，242300421410，进化视角下蛋白质结合位点多层次深度注释研究与应用，2024/01-2025/12，在研，主持

(3) 河南省科技攻关项目，192102310478，高通量抗原抗体结合位点的分析与预测，2019/01-2020/06，已结题，主持

四、代表性论文

[1] J. Zhang*, J. Qian, P. Wang, X. Liu, F. Zhang, H. Chai, Q. Zou*. Explainable deep multi-level attention learning for predicting protein carbonylation sites. Advanced Science, 2025, 2500581.

[2] Y. Wang, H. Zhu, Y. Wang, Y. Yang, Y. Huang*, J. Zhang*, K. Wong, X. Li*. EnrichRBP: an automated and interpretable computational platform for predicting and analysing RNA-binding protein events. Bioinformatics, 2025, 41(1), btaf018.

[3] J. Zhang*, S. Basu, L. Kurgan*. HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins. Nucleic Acids Research, 2024, 52(2): e10-e10.

[4] J. Zhang*, S. Basu, F. Zhang, L. Kurgan*. MERIT: Accurate prediction of multi ligand-binding residues with hybrid deep transformer network, evolutionary couplings and transfer learning, Journal of Molecular Biology, 2024, 168872.

[5] J. Li, S. He, J. Zhang, F. Zhang, Q. Zou, F. Ni*. T4Seeker: a hybrid model for type IV secretion effectors identification, BMC biology, 2024, 22:259.

[6] F. Zhang, M. Li, Jian Zhang, L. Kurgan*. HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins. Nucleic Acids Research, 2023. 51(5): e25-e25.

[7] J. Zhang*, F. Zhou, X. Liang, G. Yang. SCAMPER: accurate type-specific prediction of calcium-binding residues using sequence-derived features, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023, 20(2), 1406-1416.

[8] F. Zhang, M. Li, J. Zhang, L. Kurgan*. DeepPRObind: Modular deep learner that accurately predicts structure and disorder-annotated protein binding residues, Journal of Molecular Biology, 2023, 435(14): 167945.

[9] J. Zhang*, S. Ghadermarzi, A. Katuwawala, L. Kurgan*. DNAgenie: accurate prediction of DNA type specific binding residues in protein sequences, Briefings in Bioinformatics, 2021, 22 (6), 1-14.

[10] J. Zhang, L. Kurgan. Prediction of protein-binding residues: dichotomy of sequence-based methods developed using structured complexes vs. disordered proteins, Bioinformatics, 2020, 36 (18), 4729-4738.

[11] F. Zhang, W., Jian Zhang, M. Zeng, M. Li, L. Kurgan. PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection, Bioinformatics, 2020, 36(2), i735-i744.

[12] J. Zhang, Z. Ma, L. Kurgan. Comprehensive review and empirical analysis of hallmarks of DNA, RNA, and protein binding residues in protein chains, Briefings in Bioinformatics, 2019, 20 (4), 1250-1268.

[13] J. Zhang, L. Kurgan. SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences, Bioinformatics, 2019, 35(14): i343-i353.

[14] J. Zhang, L. Kurgan. Review and comparative assessment of sequence-based predictors of protein-binding residues, Briefings in Bioinformatics, 2018, 19 (5), 821-837.

[15] J. Zhang, H. Chai, B. Gao, G. Yang, Z. Ma. HEMEsPred: structure-based ligand-specific heme binding residues prediction by using fast-adaptive ensemble learning scheme, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018, 15(1), 147-156.

五、奖励荣誉

(1) 河南省教育厅自然科学科技成果奖一等奖，排名第一，2024年6月

(2) 信阳市青年科技奖，中共信阳市委组织部，排名第一，2023年12月

(3) 信阳市优秀青年科技专家，中共信阳市委组织部，排名第一，2023年12月