華中農(nóng)業(yè)大學(xué)

華中農(nóng)業(yè)大學(xué)信息學(xué)院大數(shù)據(jù)科學(xué)系研究生導(dǎo)師介紹如下：

章文，男，博士， 華中農(nóng)業(yè)大學(xué) 信息學(xué)院 教授 博士生導(dǎo)師

郵箱：zhangwen@mail.hzau.edu.cn

zhangwen@whu.edu.cn(使用中)

工作單位：華中農(nóng)業(yè)大學(xué)信息學(xué)院

研究方向：數(shù)據(jù)挖掘，生物信息，人工智能，機(jī)器學(xué)習(xí)

教育經(jīng)歷

2015.2-2016.2 美國(guó)麻省醫(yī)學(xué)院  訪問(wèn)學(xué)者

2006.9-2009.6 武漢大學(xué)計(jì)算機(jī)學(xué)院、新加坡國(guó)立大學(xué)聯(lián)合培養(yǎng) 博士

2003.9-2006.6 武漢大學(xué)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院 碩士

1999.9-2003.6 武漢大學(xué)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院 本科

主要職歷

2018.11-至今 華中農(nóng)業(yè)大學(xué) 信息學(xué)院 教授 博士生導(dǎo)師

2012.12-2018.10 武漢大學(xué)計(jì)算機(jī)學(xué)院 副教授 珞珈青年學(xué)者

2009-2012.11 武漢大學(xué)計(jì)算機(jī)學(xué)院 講師

科研成果

在生物信息學(xué)、數(shù)據(jù)挖掘的交叉領(lǐng)域，發(fā)表論文50余篇，含多篇計(jì)算機(jī)ESI高被引論文。擔(dān)任國(guó)內(nèi)多個(gè)學(xué)會(huì)的專業(yè)委員會(huì)委員，包括：中國(guó)人工智能學(xué)會(huì)生物信息學(xué)與人工生命專業(yè)委員會(huì)、中國(guó)計(jì)算機(jī)學(xué)會(huì)生物信息學(xué)專業(yè)委員會(huì)、中國(guó)生物信息學(xué)學(xué)會(huì)生物醫(yī)學(xué)數(shù)據(jù)挖掘與計(jì)算專業(yè)委員會(huì)等。擔(dān)任多個(gè)CCF推薦國(guó)際會(huì)議程序委員會(huì)委員，包括：BIBM，GIW，AAAI，APWeb-WAIM，UIC，ICIC。 擔(dān)任十多種高影響因子期刊審稿人。主持國(guó)家自然科學(xué)基金青年項(xiàng)目、面上項(xiàng)目和多個(gè)省部級(jí)科研項(xiàng)目。

實(shí)驗(yàn)室簡(jiǎn)介：面向生物醫(yī)學(xué)數(shù)據(jù)，研究矩陣分解、表達(dá)學(xué)習(xí)、圖學(xué)習(xí)，網(wǎng)絡(luò)缺失邊預(yù)測(cè)等數(shù)據(jù)挖掘方法、機(jī)器學(xué)習(xí)模型，探索藥物副作用、藥物靶點(diǎn)、藥物-藥物反應(yīng)、藥物-疾病關(guān)系、微生物-疾病關(guān)系、腦科學(xué)與人工智能等科學(xué)問(wèn)題，發(fā)現(xiàn)具有價(jià)值的知識(shí)和信息。

2019年開(kāi)始在信息學(xué)院招生，歡迎碩士研究生、博士研究生、博士后和本科生加入課題組。有意者，請(qǐng)通過(guò)郵件聯(lián)系。

學(xué)生要求：

1 計(jì)算機(jī)、數(shù)學(xué)或者生物信息學(xué)背景。

2 有一定編程基礎(chǔ)，python 或者 matlab，或者能夠自學(xué)掌握。

3 對(duì)于科學(xué)研究有比較濃厚的興趣，刻苦努力。

武漢大學(xué)指導(dǎo)學(xué)生和科研情況點(diǎn)擊http://www.bioinfotech.cn

1. 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. (數(shù)學(xué)與計(jì)算生物學(xué)領(lǐng)域SCI一區(qū)，CCF B類)

2. 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. (CCF B類會(huì)議)

3. 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. (CCF B類會(huì)議)

4. 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. (CCF B類會(huì)議)

5. 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. (SCI三區(qū), CCF C類)

6. 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 (SCI三區(qū))

7. Wen Zhang*, Weiran Lin, Ding Zhang, Siman Wang, Jingwen Shi, Yanqing Niu. Recent advances in the machine learning-based drug-target interaction prediction. 2018, Current drug metabolism (in press, SCI三區(qū))

8. 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. (SCI二區(qū))

9. 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 (SCI二區(qū), CCF C類)

10. Wen Zhang*, Yanlin Chen, Dingfang Li. Drug-target interaction prediction through label propagation with linear neighborhood information. Molecules, 2017, 22(12):2056 (SCI三區(qū))

11. 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 (SCI二區(qū), CCF C類，計(jì)算機(jī)類ESI高被引論文)

12. 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 (CCF B類會(huì)議)

13. 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 (CCF B類會(huì)議)

14. 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 (SCI三區(qū), CCF C類)

15. 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) (SCI三區(qū))

16. 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類會(huì)議)

17. 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類會(huì)議)

18. 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(SCI三區(qū), CCF C類，計(jì)算機(jī)類ESI高被引論文)

19. 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. (SCI三區(qū), CCF C類)

20. 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, 11(4):e0153268. (SCI三區(qū))

21. 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類會(huì)議)

22. Wen Zhang*, Feng Liu, Longqiang Luo, Jingxia Zhang, Predicting drug side effects by multi-label learning and ensemble learning. BMC Bioinformatics. 2015, 16:365 (SCI三區(qū), CCF C類)

23. 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. (SCI二區(qū), CCF C類)

24. 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. (SCI三區(qū))

25. Wen Zhang, Yanqing Niu, Yi Xiong, Meng Ke. Prediction of conformational B cell epitopes(專著邀請(qǐng)章節(jié)). “Immunoinformatics”, (Series Editor: John Walker), 2014. (專著, Springer出版，第二版). Springer, pp 185-196, New York, 2014/6/27

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

27. 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類會(huì)議)

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, 7(8): e43575,2012年8月(SCI三區(qū))

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, 6 (5): 557-569, 2012年9月(SCI四區(qū))

30. 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, 2012年6月(SCI三區(qū))

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. 2011, PLOS One, 6:e28440, (SCI三區(qū))

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, 12:341, 2011年8月(SCI三區(qū))

33. Wen Zhang*, Juan Liu, Yanqing Niu. Quantitative prediction of MHC-II binding affinity using particle swarm optimization. Artificial intelligence in medicine, 50(2): 127-132, 2010年10月, (SCI三區(qū))

34. Wen Zhang*, Juan Liu, Yanqing Niu. Quantitative prediction of MHC-II peptide binding affinity using relevance vector machine. Applied Intelligence,31(2): 180-187,2009年9月, (SCI三區(qū))

35. Wen Zhang*, Juan Liu, Yanqing Niu, Lian Wang, Xihao Hu. A Bayesian regression approach to the prediction of MHC-II binding affinity. Computer Methods and Programs in Biomedicine, 92(1):1-7,2008年6月, (SCI三區(qū))