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Zhang Wen's Homepage

Introduction:

  • Wen Zhang
    Ph.D, Professor
    College of Informatics, Huazhong Agricultural University
    Wuhan 430070, China
    Email: zhangwen(a)mail.hzau.edu.cn or zhangwen(a)whu.edu.cn,please replace (a) wtih @
    full publication list at My Google Scholar
    My ORCID
    My Publons Profile
  • Looking for Master students, Ph.D. student, Posdoctor in data mining, machine learning learning and bioinformatics

Research Interests:

  • Graph learning algorithms and their applications in biomedical big data mining
  • Recommender system algorithms and their applications in biomedical big data mining
  • Deep learning algorithms and their applications in biomedical big data mining
  • Ensmeble learning algorithms and their applications in biomedical big data mining
  • drug side effect prediction,drug-target interaction prediction, drug-drug interaction prediction,drug-disease association prediction
  • Guest editor for Frontiers in Genetics special issue “Graph-Based Learning and its Application for Biomedical Data Analysis”, call for papers :https://www.frontiersin.org/research-topics/10819/graph-based-learning-and-its-application-for-biomedical-data-analysis。


Work Experiences:

  • 2018.11-present, College of Informatics, Huazhong Agricultural University, professor
  • 2012.11-2018.10, School of Computer, Wuhan University, associate professor
  • 2015.02-2016.02, University of Massachusetts Medical School, visiting scholar
  • 2009.09-2012.11, School of Computer, Wuhan University, assistant professor

Education:

  • 2006.09-2009.06, School of Computer, Wuhan University, Ph.D.
  • 2007.09-2008.08, School of Computing, National university of Singapore, visting Ph.D. student
  • 2003.09-2006.06, School of Mathematics and Statistics, Wuhan University, Master
  • 1999.09-2003.06, School of Mathematics and Statistics, Wuhan University, Bachelor

Academic Service:

  • China Computer Federation (CCF) Senior Member
  • China Computer Federation (CCF) YOCSEF Wuhan Academic Member
  • Committee Member, China Computer Federation, Technical Committee on Bioinformatics
  • Standing Committee Member, Chinese Association for Artificial Intelligence, Technical Committee on Bioinformatics and Artificial life
  • Reviewer for conferences “AAAI”, “BIBM” and journals “Bioinformatics”, “Briefings in Bioinformatics” “Oncotarget”, “Molecular Therapy-Nucleic Acids”, “BMC Bioinformatics”, “Scientific Reports”, “PLOS ONE”, “Neurocomputing”, “Artificial Intelligence in Medicine” and “Journal of Biomedical Informatics”, “Applied Informatics”, “Neural Computing and Applications”.

Selected Publications: (*Corresponding author,#equal contribution)

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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. Bioinformtics, 15 Feb 2020, 36 (4):1241-1251.
  6. 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
  7. 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)
  8. 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
  9. 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
  10. 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.
  11. 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.
  12. Wen Zhang*, Chenglin Yu, Xiaochan Wang, Feng Liu. Predicting CircRNA-disease Associations through Linear Neighborhood Label Propagation Method. IEEE Access, 2019, 10.1109/ACCESS.2019.2920942.
  13. 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).
  14. 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, 2019, gkz711
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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
  22. 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.
  23. 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
  24. Wen Zhang*, Yanlin Chen, Dingfang Li. Drug-target interaction prediction through label propagation with linear neighborhood information. Molecules, 2017, 22(12), 2056
  25. 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
  26. 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.
  27. 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.
  28. 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.
  29. 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
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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
  35. 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
  36. Wen Zhang*, Feng Liu, Longqiang Luo, Jingxia Zhang, Predicting drug side effects by multi-label learning and ensemble learning. BMC Bioinformatics. 2015, 16:365.
  37. 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.
  38. 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.
  39. 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)
  40. 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,
  41. 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,
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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.

Software


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

  • Students:


  • in Wuhan University
  • in Huazhong Agricultural University


  • last modification date:2019.9.1