个人简历

王晗，男，吉林大学与美国密苏里大学联合培养计算生物学专业博士，副教授，博士生导师。现就任于东北师范大学信息科学与技术学院，组建东北师范大学计算生物研究所任所长。主要研究领域为计算生物学、精准医学、AI药物研发等医工交叉方向，长期致力于采用人工智能等信息学技术解决生物医药学领域科学和应用问题，涉及基于大数据的人工智能、机器学习算法在生物医药大数据系统平台建设、人类复杂疾病精准医学和跨膜蛋白靶点药物作用机制研究与应用。先后主持多项国家自然科学基金、国防科技、吉林省重点研发专项等多项国家及省部级科研项目，参与国家科技部精准医疗重点专项。同时具备丰富的软件系统开发经验，曾专职从事软件项目研发，主持开发多项大型软件系统并投入市场应用。国内外科研领域合作密切，前期应用成果包括：美国密苏里大学生命科学中心合作蛋白结构预测平台MUFOLD（http://www.mufold.org/），上海交通大学合作精神疾病新发突变知识库PsyMuKB（http://www.psymukb.net/），美国纽约凯瑟琳癌症研究所癌症精准医疗平台cBioportal（http://www.cbioportal.org/）。在本领域知名期刊发表学术论文30余篇，获得生物医疗大数据平台发明专利2项。 教育背景： 2009/08–2011/08，美国密苏里大学，生命科学中心，联合培养博士研究生 2008/09–2012/12，吉林大学，计算机科学与技术学院，计算生物学专业，博士研究生 2005/09–2008/06，吉林大学，计算机科学与技术学院，计算生物学专业，硕士研究生 1998/09–2002/07，吉林大学，计算机科学系，软件专业，本科 主要科研项目成果： 1.中国国家自然科学面上基金项目，62372099，基于深度几何学习的药物靶点跨膜蛋白结合作用域结构指纹研究，2024/01-2027/12，50 万元，主持，在研 2.吉林省科技发展计划重点研发项目，20230201090GX，分子结构与药理特征融合的人工智能新药筛选系统研发，2023/01-2025/12，80万元，主持，在研 3.吉林省科技发展计划技术创新引导项目，20230401092YY，基于组学标志物的老年机能康复体外诊断关键技术研究，2023/01-2025/12，90万元，主要执行人，在研 4.吉林省发改委产业技术研究与开发项目，2022C043-2，药物作用关键通路识别研究，2022/01-2023/12，30万元，主要执行人，在研 5.吉林省科技厅国际合作项目，20180414006GH，基于多源生物大数据融合的新型药物靶点作用预测平台，25 万元，2018/01-2020/12，结项，主持 6.中国国家自然科学青年基金项目，61402098，人类膜蛋白交互组网络构建，2015/01-2017/12，26 万元，结项，主持 论文成果： 1.Shi, L., B. Hai, Z. Kuang, H. Wang and J. Zhao (2024). "ResnetAge: A Resnet-Based DNA Methylation Age Prediction Method." Bioengineering 11(1): 34. 2.Wang, H., S. Wang, X. Ouyang, J. Zhao, Z. He and T. Gao* (2023). Predicting Protein-Ligand Binding Affinity with Multi-Scale Structural Features. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). (CCF推荐B类会议) 3.Wang, H., R. Cai, X. Zong, Z. He and L. Zhang (2023). MSCAP: DNA Methylation Age Predictor based on Multiscale Convolutional Neural Network. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). (CCF推荐B类会议) 4.Bao, Y., Y. Guo, W. Li, G. N. Lin, Z. Sun and H. Wang (2023). Probing Transmembrane Proteins Binding Domain via Multi-level Molecule Learning. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). (CCF推荐B类会议) 5.Gao T, Zhao Y, Zhang L, et al. Secondary and Topological Structural Merge Prediction of Alpha-Helical Transmembrane Proteins Using a Hybrid Model Based on Hidden Markov and Long Short-Term Memory Neural Networks[J]. Int J Mol Sci, 2023, 24(6): 5720. (IF: 50314，中科院二区，Q1) 6.Chen Q, Guo Y, Jiang J, et al. The Relative Distance Prediction of Transmembrane Protein Surface Residue Based on Improved Residual Networks[J]. Mathematics, 2023, 11(3). (IF: 2.421，中科院二区，Q1) 7. Yang Y, Yu J, Liu Z, et al. An Improved Topology Prediction of Alpha-Helical Transmembrane Protein Based on Deep Multi-Scale Convolutional Neural Network[J]. IEEE/ACM Trans Comput Biol Bioinform, 2022, 19(1): 295-304. (IF: 3.015，CCF推荐B类期刊，Q1) 8.Wang H, Zhu H, Li W, et al. Predicting Compound-Protein Interaction by Deepening the Systemic Background via Molecular Network Feature Embedding[C]. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022: 346-353. (CCF推荐B类会议) 9.Qu J, Yin S S, Wang H. Prediction of Metal Ion Binding Sites of Transmembrane Proteins[J]. Comput Math Methods Med, 2021, 2021: 2327832. 10.Zhe Liu, Yingli Gong, Yihang Bao, Yuanzhao Guo, Han Wang*, and Ganning Lin*. TMPSS: A Deep Learning-Based Predictor for Secondary Structure and Topology Structure Prediction of Alpha-Helical Transmembrane Proteins[J]. Frontiers in Bioengineering and Biotechnology, 2021, 8. (IF: 3.644，中科院二区，Q1) 11.Liu Z, Gong Y, Guo Y, et al. TMP- SSurface2: A Novel Deep Learning-Based Surface Accessibility Predictor for Transmembrane Protein Sequence[J]. Front Genet, 2021, 12(328): 656140. (IF: 4.8，中科院二区，Q2) 12.Bao Y, Wang W, Dong M, et al. Discover the Binding Domain of Transmembrane Proteins Based on Structural Universality[C]. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021: 5-11. (CCF推荐B类会议) 13.Yanwen Li, Feng Pu, Yu Feng, J. Ji, H. Sun, and Han Wang*. MRMD-Palm: A novel method for the identification of palmitoylated protein[J]. Chemometrics and Intelligent Laboratory Systems, 2021: 104245. (IF: 2.895，Q1) 14.Dong Song, Xiaxia Man, Meng Jin, Qian Li, Han Wang*, and Ye Du*. A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy[J]. Front Oncol, 2020, 10: 592556. (IF: 4.848，中科院二区，Q2) 15.Han Wang, Jiuhong Jiang, Qiufen Chen, Chunhua Zhang, Chang Lu*, and Zhiqiang Ma*. SeqTMPPI: Sequence-Based Transmembrane Protein Interaction Prediction[C]. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020: 96-99. (CCF推荐B类会议) 16.Lu Chang, Jiang Wenjie, Wang Hang, Jiang Jinxiu, Ma Zhiqiang, Wang Han*. Computational Identification and Analysis of Ubiquinone-Binding Proteins[J]. Cells, 2020, 9(2). (IF: 5.656，中科院二区，Q1) 17.Lu Chang, Gong Yingli, Liu Zhe, Guo Yuanzhao, Ma Zhiqiang, Wang Han*. TM-ZC: A Deep Learning-Based Predictor for the Z-Coordinate of Residues in α-Helical Transmembrane Proteins[J]. IEEE Access, 2020, 8: 40129-40137. (IF: 4.098，中科院二区，Q1) 18.Fang Chao, Jia Yajie, Hu Lihong, Lu Yinghua, Wang Han*. IMPContact: An Interhelical Residue Contact Prediction Method[J]. BioMed Research International, 2020, 2020: 1-10. (IF: 2.197) 19.Lin Guan Ning, Guo Sijia, Tan Xian, Wang Weidi, Qian Wei, Song Weichen, Wang Jingru, Yu Shunying, Wang Zhen, Cui Donghong, Wang Han*. PsyMuKB: An Integrative De Novo Variant Knowledge Base for Developmental Disorders[J]. Genomics Proteomics Bioinformatics, 2019, 17(4): 453-464. (IF: 6.615，中科院一区，Q1) 20.Wang Han, Yang Yuning, Yu Jiawen, Wang Xi, Zhao Dawei, Xu Dong, Sun Pingping. DMCTOP: Topology Prediction of Alpha-Helical Transmembrane Protein Based on Deep Multi-Scale Convolutional Neural Network[J]. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019: 36-43. (CCF推荐B类会议) 21.Lu Chang, Liu Zhe, Kan Bowen, Gong Yingli, Ma Zhiqiang, Wang Han*. TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues[J]. Crystals, 2019, 9(12): 640. (IF: 2.642, Q2) 22.Lu, C.; Liu, Z.; Zhang, E.; He, F.; Ma, Z.; Wang, H*., MPLs-Pred: Predicting Membrane Protein-Ligand Binding Sites Using Hybrid Sequence-Based Features and Ligand-Specific Models. Int. J. Mol. Sci. 2019, 20, (13). (IF: 4.183，中科院二区, Q2) 23.Gong, J.; Chen, Y.; Pu, F.; Sun, P.; He, F.; Zhang, L.; Li, Y.; Ma, Z.; Wang, H.*, Understanding Membrane Protein Drug Targets in Computational Perspective. Curr. Drug Targets 2019, 20, (5), 551-564. (IF: 2.642, Q2) 24.Wang, H.; Wang, J.; Zhang, L.; Sun, P.; Du, N.; Li, Y., A Sequential Segment Based Alpha-Helical Transmembrane Protein Alignment Method. Int J Biol Sci 2018, 14, (8), 901-906. (IF: 4.067，中科院二区, Q2) 25.Sun, P. P.; Tan, X.; Guo, S. J.; Zhang, J. B.; Sun, B. J.; Du, N.; Wang, H.*; Sun, H., Protein Function Prediction Using Function Associations in Protein-Protein Interaction Network. Ieee Access 2018, 6, 30892-30902. (IF: 4.098，中科院二区, Q1) 26.He, F.; Han, Y.; Wang, H.*; Ji, J.; Liu, Y.; Ma, Z., Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network. Journal of Electronic Imaging 2017, 26, (2). (IF: 0.78) 27.Zhang, L.; Wang, H.*; Yan, L.; Su, L.; Xu, D., OMPcontact: An Outer Membrane Protein Inter-Barrel Residue Contact Prediction Method. J. Comput. Biol. 2017, 24, (3), 217-228. (IF: 1.191) 28.Wang, H.; Liu, B.; Sun, P. P.; Ma, Z. Q., A Topology Structure Based Outer Membrane Proteins Segment Alignment Method. Mathematical Problems in Engineering 2013, 2013, 1-10. (IF: 1.082) 29.Wang, H.; He, Z.; Zhang, C.; Zhang, L.; Xu, D., Transmembrane protein alignment and fold recognition based on predicted topology. PLoS ONE 2013, 8, (7), e69744. (IF: 3.534) 30.Wang, H.; Zhang, C.; Shi, X.; Zhang, L.; Zhou, Y., Improving transmembrane protein consensus topology prediction using inter-helical interaction. Biochim. Biophys. Acta 2012, 1818, (11), 2679-86. (IF: 4.6) 31.D. Yu, C. Zhang, H. Wang, P. Qin, Characterization of the weak calcium binding of trimeric globular adiponectin, Cell Biochem. Funct., (2012). 32.Peiwu Qin, Chao Zhang, Han Wang, Dongmei Yu, Peter V. Cornish, and Dong Xu, RNA-protein distance patterns reveal the mechanism of translational attenuation, 2012 IEEE International Conference on Bioinformatics and Biomedicine. 33.Zhangxu Li, Guixia Liu, Han Wang*,et al.,Algorithm for identification of protein complexes using topological information of PPI network ,Journal of Information and Computational Science(EI), 2012, 9(12):3459- 3467 34.Liu guixia, Feng wei, Wang han, Liu lei, Zhou chunguang, Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm. Journal of Bionic Engineering Volume 6, Issue 1, March 2009, Pages 86-92 (SCI) 35.Ming Zheng, Gui-Xia Liu, Han Wang, Chun-Guang Zhou. Gene regulatory network reconstruction of P38 MAPK pathway using ordinary differential equation with linear regression analysis. The Second International Workshop on Advanced Computaional Intelligence, 2009:299-308. (ISTP) 36.Han Wang#, Gui-xia Liu, Chun-guang Zhou Lei Liu, Ming Zheng. Measuring the Similarity of Co-regulated Genes by Integrating Quantity and Tendency of Gene Expression Changing. In: The 2nd International Conference on Bioinformatics and Biomedical Engineering. Shanghai,China: Bioinformatics and Biomedical Engineering, May 2008. 1896-1900. (EI) 专利成果： 1.已授权中国国家发明专利，“多源生物大数据融合系统”，ZL 2018 1 0854569.2 2.已授权中国国家发明专利，“一种精神疾病新发突变信息知识平台”，ZL 2019 1 1365589.4 3.已受理中国国家发明专利，“预测药物和靶点蛋白质间相互作用的方法、装置和存储介质”，202410354579.5 4.已受理中国国家发明专利，“预测表观遗传学年龄的方法、装置、程序和存储介质”，202410445481.0