武汉大学生物医学大数据挖掘实验室
BioMedical Big Data Mining Lab - EN

近年来,生物信息学研究、医学研究和高通测序技术的发展,产生了海量生物医学数据,挖掘生物医学数据,成为极具挑战性的工作。实验室面向生物医学数据,研究矩阵分解、表达学习、图学习,网络缺失边预测等数据挖掘方法、机器学习模型,探索药物副作用、药物靶点、药物-药物反应、药物-疾病关系、微生物-疾病关系、脑科学与人工智能等科学问题,发现具有价值的知识和信息。

实验室主要工作包括:

  • 设计半监督学习框架下的多源数据整合方法,并将这些方法应用于解决社交网络、生物医学、数据挖掘中的热点问题;
  • 关注网络缺失边预测问题,设计了基于迁移学习和网络拓扑不变性的预测方法,并将研究成果应用于解决社交网络、生物医学、数据挖掘问题;
  • 药物副作用预测,药物靶点预测,药物-药物反应预测,药物-疾病关系挖掘、脑科学与人工智能。

实验室常年招收研究生,也欢迎本科生参与科研。
联系方式: zhangwen@whu.edu.cn

章文 博士 副教授

武汉大学计算机学院

教育背景:
武汉大学、新加坡国立大学联合培养博士生,美国麻省医学院访问学者

研究方向:
机器学习,推荐系统,生物医学大数据挖掘,脑科学与人工智能

导师简介:
武汉大学计算机学院副教授、珞珈青年学者,在生物信息学、数据挖掘的交叉领域,发表论文40余篇,含多篇ESI高被引论文。担任国内相关领域学会的专业委员会委员,包括:中国人工智能学会生物信息学与人工生命专业委员会、中国计算机学会生物信息学专业委员会、中国生物信息学学会生物医学数据挖掘与计算专业委员会等。担任多个CCF推荐国际会议程序委员会委员,包括:BIBM,GIW,AAAI,APWeb-WAIM,UIC,ICIC。 担任十多种高影响因子期刊审稿人。主持国家自然科学基金青年项目、面上项目和多个省部级科研项目。

联系方式: zhangwen@whu.edu.cn

团队成员

- 最后更新 2018年8月21日

毕业学生 - 最后更新 2018年8月21日

方灿铭
2018年硕士毕业
2018~ 武汉大学网络安全学院 博士研究生
2018年本科毕业
2018~ 美国俄亥俄州立大学 博士研究生
林蔚然
2018年本科毕业
2018~ 伊利诺伊大学厄巴纳香槟分校 硕士研究生
张韻秋
2018年本科毕业
2018~ 康奈尔大学 硕士研究生
刘若琦
2018年本科毕业
2018~美国俄亥俄州立大学 硕士研究生
屈乾龙
2017年本科毕业
2017~ 加州大学圣迭戈分校 硕士研究生
龙潇
2017年 本科毕业
2017~ 清华大学 硕士研究生

发表论文

* 表示通讯作者

  1. 1. Wen Zhang, Weitai Yang, XIaoting Lu, Feng Huang, Fei Luo. The Bi-direction Similarity Integration Method for Predicting Microbe-disease Associations. IEEE Access, IEEE Access, v 6, p 38052-38061, June 29, 2018

  2. 2. 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, BMC Bioinformatics 19(1), DOI: 10.1186/s12859-018-2220-4

  3. 3. 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, in press, https://doi.org/10.1016/j.ymeth.2018.06.001

  4. 4. 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

  5. 5. Wen Zhang*, Yanlin Chen, Dingfang Li. Drug-target interaction prediction through label propagation with linear neighborhood information. Molecules, 2017, 22(12):2056

  6. 6. 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, 273:526-534 (ESI 高被引)

  7. 7. 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, 2017 (CCF B 类会议)

  8. 8. 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, 2017 (CCF B 类会议)

  9. 9. 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

  10. 10. 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(S6)

  11. 11. 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 , IF= 2.435 (ESI 高被引)

  12. 12. 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. (CCF B 类会议, full paper)

  13. 13. 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. (CCF B 类会议, full paper)

  14. 14. 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, IF= 2.435

  15. 15. 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. IF=3.057

  16. 16. 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 (CCF B 类会议, full paper)

  17. 17. Wen Zhang *, Feng Liu, Longqiang Luo, Jingxia Zhang, Predicting drug side effects by multi-label learning and ensemble learning. BMC Bioinformatics. 2015, 16:365, IF= 2.435

  18. 18. 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, IF= 2.392

  19. 19. 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. Epub 2015 May 28, IF=3.057

  20. 20. Zou, Hua; Lin, Fu; Han, Jie, Wen Zhang *. GPU-Based Medical Visualization for Large Datasets, Journal of Medical Imaging and Health Informatics, Volume 5,Number 7, November 2015, pp. 1467-1473(7), IF= 0.877

  21. 21. 谢倩倩, 李订芳, 章文 *. 基于集成学习的离子通道药物靶点预测 [J]. 计算机科学, 2015,42(4):177-180

  22. 22. 谢倩倩, 李订芳, 章文 *. 两种基于树结构的基因选择算法. 计算机科学, 2015,42(7):250-253

  23. 23. Zhang Wen *; Ke, Meng. Protein encoding: A Matlab toolbox of representing or encoding protein sequences as numerical vectors for bioinformatics. Journal of Chemical and Pharmaceutical Research 6, pp 2000-2007, 2014/6/7 (EI)

  24. 24. Zhang Wen *, Yanqing Niu, Yi Xiong, Meng Ke. Prediction of conformational B cell epitopes(专著邀请章节). “Immunoinformatics”, (Series Editor: John Walker), 2014. (专著, Springer 出版,第二版 ). Springer, pp 185-196, New York, 2014/6/27

  25. 25. Juan Liu, Wen Zhang. Databases for B cell epitopes(专著邀请章节). An invited Chapter in the second edition of the book titled “Immunoinformatics”, under the series titled “Methods in Molecular Biology” (Series Editor: John Walker). (专著, Springer 出版,第二版) .Springer, pp 135-148, New York, 2014/6/27

  26. 26. 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. (CCF B 类会议, full paper)

  27. 27. 许逸格,张可,柯萌,谢倩倩,章文 (通讯作者). 一种基于循环回归的推荐算法. 华中科技大学学报, 第 41 卷,第 S2 期,195-198 页, 2013. (EI)

  28. 28. 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, 2012, 7(8): e43575, IF=3.057

  29. 29. 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, IF= 0.528

  30. 30. Yi Xiong, Juan Liu, Wen Zhang, Tao Zeng. Prediction of heme binding residues from protein sequences with integrative sequence profiles. Proteome Science, 2012,(Suppl 1): S20, IF= 1.746

  31. 31. 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,2012, 6:e28440, IF=3.057

  32. 32. 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, 2011,12:341, IF= 2.435

  33. 33. 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, IF= 2.142

  34. 34. 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, IF= 1.215

  35. 35. 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, IF= 1.862

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