[22] Fang Ge, Cui-Feng Li, Chao-Ming Zhang, Ming Zhang, and Dong-Jun Yu. PRITrans: A Transformer-based Approach for the Prediction of the Effectes of Missense Mutation on Protein-RNA Interactions [J]. International Journal of Molecular Sciences, 2024, In Press. (SCI 2区) 

[21] Ming Zhang, Jianren Zhou, Xiaohua Wang, Xun Wang and Fang Ge. DeepBP: Ensemble deep learning strategy for bioactive peptide prediction[J].BMC Bioinformatics (2024) 25:352. (SCI 3区) 

[20] Ming Zhang, Xiaohua Wang, Shanruo Xu, Fang Ge, Ian Costa Paixao4,Jiangning Song, Dong-Jun Yu. MetalTrans: A Biological Language Model-Based Approach for Predicting Disease-Associated Mutations in Protein Metal-Binding Sites[J].  Journal of Chemical Information and Modeling. 2024(64):6216−6229. (SCI 2区 Top / Cover paper)

[19] Ming Zhang, Chao Gong, Fang Ge, and Dong-Jun Yu. FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combinationwithin a Transformer Framework. Journal of Chemical Information and Modeling. 2024(64): 1394−1406. (SCI 2区Top)

[18] Yongle Xu, Ming Zhang, and Boyin Jin. Pursuing Benefits or Avoiding Threats: Realizing Regional Multi-Target Electronic Reconnaissance With Deep Reinforcement Learning. IEEE ACCESS. 2023(11):63972-83984. (SCI 3区)

[17] Hong Sun, Wanrong Cao and Ming Zhang. Energy stability of a temporal variable-step difference scheme or time-fractional nonlinear fourth-order reaction-diffusion equation. International Journal of Computer Mathematics, 2023,100(5):991-1008. (SCI 4区)

[16] 张明, 徐妍, 陈韬, 王长宝, 於东军. 基于核酸物化属性显著性约简的m6A位点识别[J]. 南京理工大学,2019,43(2)：199-208.(中文核心)

[15] 孙佳伟, 张明*, 王长宝, 徐维艳, 程科, 段先华. 一种新的融合统计特征的DNA甲基化位点识别方法[J]. 江苏科技大学学报, 2019,33(2)：62-68.(中文核心)

[14] Ming Zhang,Yan Xu, Lei Li, Zi Liu, Xibei Yang, Dong-Jun. Accurate RNA 5-methylcytosine site prediction based on heuristic physical-chemical properties reduction and classifier ensemble[J]. Analytical Biochemistry, 2018, 550: 41-48. (SCI收录)

[13] Ming Zhang , Jia-Wei Sun, Zi Liu, Ming-Wu Ren, Hong-Bin Shen, and Dong-Jun Yu. Improving m6A Sites Prediction with Heuristic Selection of Nucleotide Physical-chemical Properties[J]. Analytical Biochemistry, 2016, 508: 104-113. (SCI收录)

[12] Jun Hu, Yang Li, Ming Zhang, Xibei Yang, Hong-Bin Shen, and Dong-Jun Yu. Predicting Protein-DNA Binding Residues by Weightedly Combining Sequence-based Features and Boosting Multiple SVMs. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2016. (SCI收录)

[11]张明，程科，杨习贝，唐振民. 基于加权粒度的多粒度粗糙集[J]. 控制与决策，2015, 30(2): 222-228.（EI收录）;

[10] 张明, 孙佳伟, 程科, 徐维艳, 潘磊. 一种新的广义多粒度优势关系粗糙集[J]. 江苏科技大学学报（自然科学版）, 2015, 29(6): 560-566. (中文核心)

[9] Ming Zhang, Weiyan Xu, Xibei Yang, Zhenmin Tang. Incomplete Variable Multigranulation Rough Sets Decision, Applied Mathematics & Information Sciences[J], 2014, 8(3): 1159-1166. (SCI收录)

[8] Ming Zhang, Zhenmin Tang, Weiyan Xu, Xibei Yang. A Variable Multigranulation Rough Sets Approach, Lecture Notes in Computer Science, 6840(2012), 315–322. (EI收录).

[7]张明,唐振民, 杨习贝. 可变多粒度粗糙集模型. 模式识别与人工智能[J]. 2012, 25(4): 709-720. (EI收录). 

[6] Xibei Yang, Ming Zhang, Huili Dou, Jingyu Yang. “Neighborhood systems-based rough sets in incomplete information system”. Knowledge-Based Systems, 2011, 24(6):858-867. (SCI收录)

[5] 张明,唐振民, 杨习贝. 不完备信息系统中的否定决策规则和知识约简. 控制与决策[J] 2011, 26(6): 851-856. (EI收录).

[4]张明, 唐振民, 杨习贝. 肯定和否定决策规则的获取及约简[J]. 系统工程与电子技术. 2011, 33(9): 2230-2234. (EI收录).

[3] 张明, 唐振民, 徐维艳, 杨习贝. 可变粒度粗糙集. 计算机科学[J]. 2011, 38(10): 220-222. (中文核心)

[2] 张明, 唐振民, 杨习贝, 徐维艳. 基于粗糙集的拒绝决策规则获取和约简[J]. 计算机工程. 2011, 37(3):22-24. (中文核心)

[1] 张明, 刘小兵, 韩斌. 一种新型除铁器测试仪[J]. 仪表技术与传感器, 2010(3),31-33. (中文核心)