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章文的个人主页
Zhang Wen's Homepage

个人简介:

  • 章文,华中农业大学信息学院教授,博士生导师
    中国计算机学会(CCF)高级会员,CCF YOCSEF武汉副主席
    中国人工智能学会生物信息学与人工生命专委会常务委员
    中国计算机学会生物信息学专委会委员
    湖北省生物信息学会理事
    电子邮件:zhangwen(a)mail.hzau.edu.cn 或者 zhangwen(a)whu.edu.cn(为防止垃圾邮件,请把(a)换成@)

  • 章文老师长期从事人工智能算法及其在生物医学大数据分析方面的研究,在IJCAI,Information Sciences, PLOS Computational Biology, Bioinformatics, Briefings in Bioinformatics, IEEE-ACM Transactions on Computational Biology and Bioinformatics, BMC Bioinformatics, Neurocomputing等杂志和中国计算机学会推荐的国际会议上发表论文60余篇,含多篇计算机ESI高被引论文。担任多个中国计算机学会推荐国际会议程序委员会委员,包括BIBM,GIW,AAAI,APWeb-WAIM,UIC等。担任多个重要期刊的审稿人,包括Neural networks,Bioinformatics, Briefings in Bioinformatics, Molecular Therapy-Nucleic Acids, BMC Bioinformatics, Neurocomputing等。 先后主持/完成国家自然科学基金青年项目(1项)、面上项目(2项)和多个省部级科研项目。

    Full publication list at My Google Scholar
    My ORCID
    My Publons Profile
  • 招收计算机、数学、生物信息学背景的硕士研究生(招生专业:计算机科学与技术、电子信息、生物信息学),博士研究生(招生专业:生物信息学、农业信息工程),博士后;欢迎相关本科生参与科研。 感兴趣的同学欢迎邮件联系,署名邮件必回复。


    查看学生去向


研究方向(微信关注AI in Graph公众号了解):

  • 图学习/知识图谱算法研究及其在生物医学大数据的应用
  • 推荐系统算法研究及其在生物医学大数据的应用
  • 深度学习算法研究及其生物医学大数据的应用
  • 集成学习算法研究及其生物医学大数据的应用

工作经历:

  • 2018年11月-至今:华中农业大学,信息学院,教授
  • 2012年12月-2018年10月:武汉大学,计算机学院,副教授
  • 2014年1月-2018年10月:武汉大学,计算机学院,珞珈青年学者
  • 2015年2月-2016年2月:美国麻省医学院 访问学者
  • 2009年9月-2012年11月:武汉大学,计算机学院,讲师

教育经历:

  • 2006年9月-2009年6月: 武汉大学,计算机学院,博士研究生
  • 2007年9月-2008年8月: 新加坡国立大学,计算机学院,访问学生
  • 2003年9月-2006年6月: 武汉大学,数学与统计学院,硕士研究生
  • 1999年9月-2003年6月: 武汉大学,数学与统计学院,本科生

代表论文: (*Corresponding author,#equal contribution)

  1. Haitao Fu†, Feng Huang†, Xuan Liu, Yang Qiu, Wen Zhang*. MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks. Bioinformatics, 1 September 2021(in press), doi: 10.1093/bioinformatics/btab651.
  2. Shuai Liu, Xinran Xu, Zhihao Yang, Xiaohan Zhao, Shichao Liu, Wen Zhang*. EPIHC: Improving Enhancer-Promoter Interaction Prediction by using Hybrid features and Communicative learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2 September 2021(in press), doi: 10.1109/TCBB.2021.3109488.
  3. Chenshuai Zhao, Shuai Liu, Feng Huang, Shichao Liu, Wen Zhang*. CSGNN: Contrastive Self-Supervised Graph Neural Network for Molecular Interaction Prediction. The 30th International Joint Conference on Artificial Intelligence(IJCAI2021), Montreal-themed Virtual Reality, 19th -26th August, 2021 (CCF A)
  4. Menglu Li, Wen Zhang*. PHIAF: prediction of phage–host interactions with GAN-based data augmentation and sequence-based feature fusion. Briefings in Bioinformatics, 10 August 2021, doi:10.1093/bib/ bbab348.
  5. Zhankun Xiong, Feng Huang, Ziyan Wang, Shichao Liu, Wen Zhang*. A multimodal framework for improving in silico drug repositioning with the prior knowledge from knowledge graphs. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 7 August 2021(in press), doi:10.1109/TCBB.2021.310359.
  6. Yang Qiu, Yang Zhang, Yifan Deng, Shichao Liu*, Wen Zhang*. A Comprehensive Review of Computational Methods for Drug-drug Interaction Detection. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18 May 2021(in press), doi: 10.1109/TCBB.2021.3081268
  7. Zhouxin Yu#,Feng Huang#, Xiaohan Zhao, Wenjie Xiao, Wen Zhang*. Predicting Drug-Disease Associations through Layer Attention Graph Convolutional Network. Briefings in Bioinformatics, 1 September 2020, doi:10.1093/bib/bbaa243.
  8. Xiangan Chen, Shuai Liu, Wen Zhang*. Predicting Coding Potential of RNA Sequences by Solving Local Data Imbalance. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2 September 2020, doi:10.1109/TCBB.2020.3021800.
  9. Feng Huang#, Xiang Yue#, Zhankun Xiong, Zhouxing Yu,Shichao Liu, Wen Zhang*. Tensor Decomposition with Relational Constraints for Predicting Multiple Types of MicroRNA-disease Associations. Briefings in Bioinformatics, 6 June 2020, doi:10.1093/bib/bbaa140.
  10. Yifan Deng, Xinran Xu, Yang Qiu, Jingbo Xia, Wen Zhang*, Shichao Liu*. A multimodal deep learning framework for predicting drug-drug interaction events. Bioinformatics, 14 May 2020, doi:10.1093/bioinformatics/btaa501.
  11. Xiang Yue*, Zhen Wang, Jingong Huang, Srinivasan Parthasarathy, Soheil Moosavinasab, Yungui Huang, Simon M Lin, Wen Zhang, Ping Zhang, Huan Sun*. Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics, 15 Feb 2020, 36 (4):1241-1251. (计算机 ESI高被引)
  12. Jiang Li, Yawen Xue, Muhammad Talal Amin, Yanbo Yang, Jiajun Yang, Wen Zhang, Wenqian Yang, Xiaohui Niu, Hong-Yu Zhang, Jing Gong*. ncRNA-eQTL: a database to systematically evaluate the effects of SNPs on non-coding RNA expression across cancer types. Nucleic acids research, 2020, 48 (D1):D956-D963
  13. Xiaochan Wang, Yuchong Gong, Jing Yi, Wen Zhang*. Predicting gene-disease associations from the heterogeneous network using graph embedding. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), November 18-21, 2019, San Diego, CA, USA, pp:504-511(Best student paper nomination)
  14. Shuang Zhou, Xiang Yue, Xinran Xu, Shichao Liu, Wen Zhang*, Yanqing Niu*. LncRNA-miRNA interaction prediction from the heterogeneous network through graph embedding ensemble learning. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), November 18-21, 2019, San Diego, CA, USA, pp: 622-627
  15. Zeming Liu, Feng Liu*, Chengzhi Hong, Meng Gao, Yi-Ping Phoebe Chen, Shichao Liu, Wen Zhang*. Detection of Cell Types from Single-cell RNA-seq Data using Similarity via Kernel Preserving Learning Embedding. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), November 18-21, 2019, San Diego, CA, USA, pp: 451-457
  16. Wen Zhang*, Zhishuai Li, Wenzheng Guo, Weitai Yang, Feng Huang. A fast linear neighborhood similarity-based network link inference method to predict microRNA-disease associations. IEEE/ACM transactions on computational biology and bioinformatics, 29 July 2019, DOI: 10.1109/TCBB.2019.2931546.
  17. Wen Zhang*, Kanghong Jing, Feng Huang, Yanlin Chen, Bolin Li, Jinghao Li, Jing Gong. SFLLN: A sparse feature learning ensemble method with linear neighborhood regularization for predicting drug-drug interactions. Information Sciences, September 2019, 497:189-201. (计算机 ESI高被引)
  18. Wen Zhang*, Weiran Lin, Ding Zhang, Siman Wang, Jingwen Shi, Yanqing Niu. Recent advances in the machine learning-based drug-target interaction prediction. Current drug metabolism, 2019, 20(3):194-202(9).
  19. Yi-Cheng Gao, Xiong-Hui Zhou*, Wen Zhang*. An ensemble strategy to predict prognosis in ovarian cancer based on gene modules. Frontiers in genetics, 24 April 2019.
  20. Wen Zhang*, Xiang Yue, Guifeng Tang, Wenjian Wu, Feng Huang, Xining Zhang. SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions. PLoS Computational Biology, December 2018, 14(12): e1006616.
  21. Wen Zhang*, Xiaoting Lu, Weitai Yang, Feng Huang, Binlu Wang, Alan wang, and Qi Zhao. HNGRNMF: Heterogeneous Network-based Graph Regularized Nonnegative Matrix Factorization for predicting events of microbe-disease associations. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018), Madrid, Spain, Dec 3-6, 2018.
  22. Wen Zhang*, Guifeng Tang, Siman Wang, Yanlin Chen, Shuang Zhou, Xiaohong Li*. Sequence-derived linear neighborhood propagation method for predicting lncRNA-miRNA interactions. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018), Madrid, Spain, Dec 3-6, 2018.
  23. Wen Zhang*, Feng Huang, Xiang Yue, Xiaoting Lu, Weitai Yang, Zhishuai Li, Feng Liu. Prediction of Drug-Disease Associations and Their Effects by Signed Network-Based Nonnegative Matrix Factorization. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018), Madrid, Spain, Dec 3-6, 2018.
  24. Wen Zhang*, Xiang Yue, Weiran Lin, Wenjian Wu, Ruoqi Liu, Feng Huang, Feng Liu. Predicting drug-disease associations by using similarity constrained matrix factorization. BMC Bioinformatics, 2018, 19:233.
  25. Wen Zhang*, Yanlin Chen, Dingfang Li, Xiang Yue. Manifold regularized matrix factorization for drug-drug interaction prediction. Journal of biomedical informatics, 2018, 88, 90-97
  26. Wen Zhang*, Xiang Yue, Feng Huang, Ruoqi Liu, Yanlin Chen, Chunyang Ruan. Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network. Methods, 2018,145,51-59.
  27. Wen Zhang*, Xinrui Liu, Yanlin Chen, Wenjian Wu, Wei Wang, Xiaohong Li. Feature-derived Graph Regularized Matrix Factorization for Predicting Drug Side Effects. February 2018, Neurocomputing 2018, 287:154-162
  28. Wen Zhang*, Yanlin Chen, Dingfang Li. Drug-target interaction prediction through label propagation with linear neighborhood information. Molecules, 2017, 22(12), 2056
  29. Wen Zhang*, Qianlong Qu, Yunqiu Qu, Yunqiu Zhang, Wei Wang. The linear neighborhood propagation method for predicting long non-coding RNA-protein interactions. Neurocomputing, 2018, 273(17):526-534 (计算机 ESI高被引)
  30. Wen Zhang*, Xiang Yue, Yanlin Chen, Weiran Lin, Bolin Li, Feng Liu, and Xiaohong Li. Predicting drug-disease associations based on the known association bipartite network. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017), Kansan City, MO, USA, Nov 13 - Nov 16.
  31. Wen Zhang*, Jingwen Shi, Guifeng Tang, Bolin Li, Weiran Lin, Xiang Yue, Yanlin Chen, and Dingfang Li. Predicting small RNAs in bacteria via sequence learning ensemble method. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017), Kansan City, MO, USA, Nov 13 - Nov 16.
  32. Wen Zhang*, Xiaopeng Zhu, Yu Fu, Junko Tsuji, Zhiping Weng. Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods. BMC bioinformatics, 2017, 18(Suppl 13):464.
  33. Wen Zhang*, Xiang Yue, Feng Liu, Yanlin Chen, Shikui Tu, Qianlong Qu, Xining Zhang. A unified frame of predicting side effects of drugs by using linear neighborhood similarity. BMC Systems biology, 2017, 11(Suppl 6):101
  34. Wen Zhang*, Yanlin Chen; Feng Liu, Fei Luo, Gang Tian, Xiaohong Li. Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinformatics, 2017, 18: 18.(计算机 ESI高被引)
  35. Wen Zhang*, Yanlin Chen, Shikui Tu, Feng Liu, and Qianlong Qu. Drug side effect prediction through linear neighborhoods and multiple data source integration. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2016), ShenZhen, China, Dec 15-18, 2016.
  36. Wen Zhang*, Xiaopeng Zhu, Yu Fu, Junko Tsuji, and Zhiping Weng. The prediction of human splicing branchpoints by multi-label learning. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2016), ShenZhen, China, Dec 15-18, 2016.
  37. Dingfang Li, Longqiang Luo, Wen Zhang*, Feng Liu, Fei Luo. A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs. BMC Bioinformatics, 2016 17: 329.
  38. Longqiang Luo, Dingfang Li, Wen Zhang*, Shikui Tu, Xiaopeng Zhu, and Gang Tian. Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features. PLoS One. 2016 Apr 13;11(4): e0153268. 10.1137/1.9781611974348.3
  39. Ruichu Cai, Zhenjie Zhang, Srinivasan Parthasarathy, Anthony K. H. Tung, Zhifeng Hao, Wen Zhang. Multi-Domain Manifold Learning for Drug-Target Interaction Prediction. SIAM International Conference on Data Mining (SDM16), June 2016. DOI: 10.1137/1.9781611974348.3
  40. Wen Zhang*, Feng Liu, Longqiang Luo, Jingxia Zhang, Predicting drug side effects by multi-label learning and ensemble learning. BMC Bioinformatics. 2015, 16:365.
  41. Wen Zhang*, Hua Zou, Longqiang Luo, Qianchao Liu, Weijian Wu, and Wenyi Xiao. Predicting potential side effects of drugs by recommender methods and ensemble learning. Neurocomputing, 2015, 173(3):979-987.
  42. Wen Zhang*, Yanqing Niu, Hua Zou, Longqiang Luo, Qianchao Liu, Weijian Wu. Accurate prediction of immunogenic T-cell epitopes from epitope sequences using the genetic algorithm-based ensemble learning. PLoS One 2015 28;10(5): e0128194.
  43. Zou, Hua; Lin, Fu; Han, Jie; Zhang, Wen*. GPU-Based Medical Visualization for Large Datasets, Journal of Medical Imaging and Health Informatics,2015, 5(7):1467-1473(7)
  44. Wen Zhang*, Yanqing Niu, Yi Xiong, Meng Ke. Prediction of conformational B cell epitopes. An invited Chapter in the second edition of the book titled “Immunoinformatics”, under the series titled “Methods in Molecular Biology” (Series Editor: John Walker). Springer, pp 185-196, New York,
  45. Juan Liu, Wen Zhang. Databases for B cell epitopes. An invited Chapter in the second edition of the book titled “Immunoinformatics”, under the series titled “Methods in Molecular Biology” (Series Editor: John Walker). Springer, pp 135-148, New York,
  46. Wen Zhang, Juan Liu, Yi Xiong, Meng Ke, and Ke Zhang. Predicting immunogenic T-cell epitopes by combining various sequence-derived features. The IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2013). 18-21 Dec. 2013, Page(s):4-9, Shanghai, China, Dec 2013.
  47. Wen Zhang, Yanqing Niu, Yi Xiong, Meng Zhao, Rongwei Yu, Juan Liu. Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning. PLOS One, 7(8): e43575.
  48. Wen Zhang, Juan Liu, Meng Zhao, Qingjiao Li. Predicting linear B-cell epitopes by using sequence-derived structural and physicochemical features. International Journal of Data Mining and Bioinformatics, 2012, 6 (5): 557-569.
  49. Yi Xiong, Juan Liu, Wen Zhang, Tao Zeng. Prediction of heme binding residues from protein sequences with integrative sequence profiles. Proteome Science (Suppl 1): S20.
  50. Yi Xiong, X Junfeng Xia, Wen Zhang, Juan Liu. Exploiting a reduced set of weighted average features to improve prediction of DNA-binding residues from 3D Structures. PLOS One, 6: e28440.
  51. Wen Zhang, Yi Xiong, Meng Zhao, Hua Zou, Xinghuo Ye, Juan Liu. Prediction of conformational B-cell epitopes from 3D structures by random forest with a distance-based feature. BMC Bioinformatics, 12:341.
  52. Wen Zhang, Juan Liu, Yanqing Niu. Quantitative prediction of MHC-II binding affinity using particle swarm optimization. Artificial intelligence in medicine, 2010, 50(2): 127-132.
  53. Wen Zhang, Juan Liu, Yanqing Niu. Quantitative prediction of MHC-II peptide binding affinity using relevance vector machine. Applied Intelligence,2009, 31(2): 180-187.
  54. Wen Zhang, Juan Liu, Yanqing Niu, Wang Lian, Hu Xihao. A Bayesian regression approach to the prediction of MHC-II binding affinity. Computer Methods and Programs in Biomedicine, 2008, 92(1):1-7.

学术软件


  • drug-disease association prediction server: http://bioinfotech.cn/SCMFDD
  • BranchPoint Server:http://www.bioinfotech.cn/branchpoint/
  • Our GitHub: https://github.com/BioMedicalBigDataMiningLabWhu

  • 培养学生:


  • 武汉大学
  • 华中农业大学
  • 查看学生去向



    最后修改时间:2020.5.25 鄂ICP备17010423号