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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. (ÊýѧÓë¼ÆËãÉúÎïѧÁìÓòSCIÒ»Çø£¬CCF BÀà)

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 BÀà»áÒé)

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 BÀà»áÒé)

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 BÀà»áÒé)

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. (SCIÈýÇø, CCF CÀà)

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 (SCIÈýÇø)

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, SCIÈýÇø)

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¶þÇø)

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¶þÇø, CCF CÀà)

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

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¶þÇø, CCF CÀ࣬¼ÆËã»úÀàESI¸ß±»ÒýÂÛÎÄ)

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 BÀà»áÒé)

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 BÀà»áÒé)

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 (SCIÈýÇø, CCF CÀà)

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) (SCIÈýÇø)

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 BÀà»áÒé)

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 BÀà»áÒé)

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(SCIÈýÇø, CCF CÀ࣬¼ÆËã»úÀàESI¸ß±»ÒýÂÛÎÄ)

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. (SCIÈýÇø, CCF CÀà)

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. (SCIÈýÇø)

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 BÀà»áÒé)

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 (SCIÈýÇø, CCF CÀà)

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¶þÇø, CCF CÀà)

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. (SCIÈýÇø)

25. Wen Zhang, Yanqing Niu, Yi Xiong, Meng Ke. Prediction of conformational B cell epitopes(רÖøÑûÇëÕ½Ú). “Immunoinformatics”, (Series Editor: John Walker), 2014. (רÖø, Springer³ö°æ£¬µÚ¶þ°æ). Springer, pp 185-196, New York, 2014/6/27

26. Juan Liu, Wen Zhang. Databases for B cell epitopes(רÖøÑûÇëÕ½Ú). An invited Chapter in the second edition of the book titled “Immunoinformatics”, under the series titled “Methods in Molecular Biology” (Series Editor: John Walker). (רÖø, Springer³ö°æ£¬µÚ¶þ°æ) .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 BÀà»áÒé)

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): e43575,2012Äê8ÔÂ(SCIÈýÇø)

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, 2012Äê9ÔÂ(SCIËÄÇø)

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, 2012Äê6ÔÂ(SCIÈýÇø)

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, (SCIÈýÇø)

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, 2011Äê8ÔÂ(SCIÈýÇø)

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, 2010Äê10ÔÂ, (SCIÈýÇø)

34. Wen Zhang*, Juan Liu, Yanqing Niu. Quantitative prediction of MHC-II peptide binding affinity using relevance vector machine. Applied Intelligence,31(2): 180-187,2009Äê9ÔÂ, (SCIÈýÇø)

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):1-7,2008Äê6ÔÂ, (SCIÈýÇø)