王伟，男，1975年7月生，中共党员，副教授，博士，硕士生导师，韩国汉阳大学访问学者，中国计算机学会会员、中国人工智能学会会员、中国生物工程学会会员。联系邮箱：weiwang@htu.edu.cn
2011年-2014年在武汉大学计算机学院进行博士研究生学习。目前致力于数据挖掘、机器学习、深度学习、生物信息等方向研究。曾获得河南省科技成果一等奖1项，河南省自然科学优秀学术论文二等奖，河南省教育厅优秀论文奖一等奖2项、二等奖2项，河南师范大学研究生优秀指导教师，河南师范大学文明教师等奖项。获得国家发明专利7项，软件著作权9项。主持教育部协同育人创新项目、河南省自然科学基金项目、河南省科技攻关项目、河南省高校重点项目等。近几年来，担任了 Bioinformatics、Brief in Bioinformatics、BIBM、IEEE-ACM Transactions on Computational Biology and Bioinformatics、IEEE Transactions on Neural Networks and Learning Systems、IEEE Journal of Biomedical and Health Informatics等期刊会议审稿人，在生物信息领域主流的 SCI 期刊发表相关论文二十余篇，主要研究成果发表在Pattern Recognition(1区顶刊)，Engineering Applications of Artiϧcial Intelligence(1区顶刊)，Briefings in Bioinformatics (2区顶刊)，IEEE-ACM Transactions on Computational Biology and Bioinformatics (CCF B类)，Applied Soft Computing (2区顶刊)，International Journal of Biological Macromolecules (2区顶刊)等期刊，得到了国内外同行专家的认可，发表在 Proteins 期刊的文章被选为期刊封面文章。近年来，指导研究生共计30余人，其中张禹同学获得了2022年河南省优秀硕士学位论文，毕业研究生就业方向为攻读博士研究生，高等院校，优质IT企业等。
主要的论文著作
(1)Wei Wang , Gaolin Yuan, et al. MCMTSYN: Predicting anticancer drug synergy via cross-modal feature fusion and multi-task learning[J]. Pattern Recognition, 2026，76(8): 113222.
(2)Wei Wang, Yuchen Zhu, et al. Multi-view feature learning and enhanced hypergraph neural networks for synergistic prediction of drug combination. Engineering Applications of Artificial Intelligence, 2026, 167: 11386.
(3)Wei Wang , Linchong Ma, et al. AdptDilatedGCN: Protein-ligand binding affinity prediction based on multi-scale interaction fusion mechanism and dilated GCN. International Journal of Biological Macromolecules, 2025, 311: 143751.
(4)Wei Wang, Yuchen Zhu, et al. DeepKGI: Cross-layer graph fusion and interpretable key gene identification for cancer drug response prediction. Applied Soft Computing, 2026, 187(2): 114344.
(5)Wei Wang, Gaolin Yuan, et al. A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs, Briefings in Bioinformatics, 2024, 25(1):bbad522
(6)Wei Wang, Shitong Wan, et al. ResaPred: A Deep Residual Network with Self-Attention to Predict Protein Flexibility, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2025, 22(1): 216-227
(7)Wei Wang, Mengxue Yu, et al. SMGCN: multiple similarity and multiple kernel fusion based graph convolutional neural network for drug-target interactions prediction, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2024, 21(1): 143-154
(8)Wei Wang, Zhenxi Sun, et al. MAHyNet: parallel hybrid network for RNA-protein binding sites prediction based on multi-headattention and expectation pooling, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2024, 21(3): 416-427
(9)Wang Wei, Sun Bin, et al. GraphPLBR: Protein-ligand binding residue prediction with deep graph convolution network[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023.
(10)Wang Wei, Wang Yongqing, et al. LPLSG: Prediction Of LncRNA-protein Interaction Based On Local Network Structure[J]. Current Bioinformatics, 2023(8): 1-9.
(11)Wang Wei, Liang Shihao, et al. GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks[J]. Methods, 2022, 206: 101-107.
(12)Wang Wei, Zhang Yu,et al, PseAraUbi: predicting arabidopsis ubiquitination sites by incorporating the physico-chemical and structural features[J]. Plant molecular biology, 2022.110，81-92.
(13)Wang Wei, Zhang Yu, et al,Prediction of DNA-Binding Protein–Drug-Binding Sites Using Residue Interaction Networks and Sequence Feature[J]. Frontiers in Bioengineering and Biotechnology, 2022.10,822392
(14)Wang Wei,Shu Xili, Sun Bin, et al, Predicting DNA-binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature[J]. Proteins-structure function and bioinformatics.2022.11：1-11.
(15)Wang Wei, Jiao Xiaolin,etal.DeepGenBind: a novel deep learning model for predicting transcription factor binding sitesn[C].IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2022，3629-3635.
(16)Wang Wei, Wang Yongqing, et al. PPDTS: Predicting potential drug–target interactions based on network similarity[J]. IET Systems Biology, 2021,11:1-10 .
(17)Wang Wei, Sun Bin, et al. DPLA: prediction of protein-ligand binding affinity by integrating multi-level information[C]. BIBM, 2021.
(18)Wang Wei, Lv Hehe,et al. Predicting DNA binding protein-drug interactions based on network similarity[J]. BMC Bioinformatics. 2020; 21: 322.
(19) Wang Wei, Lv Hehe, et al. DLS: A Link Prediction Method Based on Network Local Structure for Predicting Drug-Protein Interactions[J]. Front Bioeng Biotechnol. 2020; 8: 330.
(20) Wang Wei, Li Keliang,et al.SmoPSI: Analysis and Prediction of Small Molecule Binding Sites Based on Protein Sequence Information[J]. Comput Math Methods Med. 2019; 2019: 1926156.
(21) Wang Wei , Zhao Yuan , et al. InPrNa: A Tool for Insight Into Protein–Nucleic Acids Interaction Information[J]. IEEE Access, 2019, 7:140375-140382.
(22)Wang Wei, Li Keliang, et al. Analyzing the Surface Structure of the Binding Domain on DNA and RNA Binding Proteins[J]. IEEE Access, 2019:1-1.
(23)Wang Wei, Sun Lin, Zhang Shiguang, et al. Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences[J]. BMC Bioinformatics, 2017, 18(1):300.
(24) Wang Wei, Liu Juan, and Sun Lin, Surface shapes and surrounding environment analysis of single‐ and double‐stranded DNA‐binding proteins in protein‐DNA interface[J]. Proteins Structure Function & Bioinformatics, 2016, 84(7):979-989.
(25) Wang Wei，Liu Juan，Zhou Xionghui，Identification of single-stranded and double-stranded DNA binding proteins based on protein structure. BMC Bioinformatics, 2014，15 Suppl 12：S4-S4
(26) Wang Wei, Liu Juan，Xiong Yi，Zhu Lida，Zhou Xionghui,Analysis andclassification of DNA-binding sites in single-stranded and double-stranded DNA-binding proteins using protein information.，IET Systems Biology，2014，8（4）：176-183
(27)王伟,刘娟,孟志斌.基于时序遥感卫星云图的对流云团动态追踪预测[J].电子学报, 2014, 42(4):804-808.
(28)王伟,刘娟,孟志斌,等.卫星云图的多通道FCM分割算法[J].计算机工程与科学, 2012, 34(10):83-87.