1. He Yan, Qiao-Lin Ye, and Dong-Jun Yu*. Efficient and robust TWSVM classification via a minimum L1-norm distance metric criterion [J]. Machine Learning, 2018, In Press.

  2. Jun Hu, Zi Liu, Dong-Jun Yu*, and Yang Zhang*. LS-align: an atom-level, flexible ligand structural alignment algorithm for efficient virtual screening [J]. Bioinformatics, 2018, DOI: 10.1093/bioinformatics/bty081, In Press.

  3. Muhammad Kabir, Muhammad Arif, Farman Ali, Saeed Ahmad, Zar Nawab Khan Swati, and Dong-Jun Yu*. Prediction of membrane protein types by exploring local discriminative information from evolutionary profiles [J]. Analytical Biochemistry, 2018, In Press.

  4. Muhammad Kabir, Muhammad Arif, Saeed Ahmad, Zakir Ali, and Dong-Jun Yu*. Intelligent computational method for discrimination of anticancer peptides by incorporating sequential and evolutionary profiles information [J]. Chemometrics and Intelligent Laboratory Systems. 2018. In press.

  5. Farman Ali, Muhammad Kabir, Muhammad Arif, Zar Nawab Khan Swati, Zaheer Ullah Khan, Matee Ullah, and Dong-Jun Yu*. DBPPred-PDSD: Machine Learning Approach for Prediction of DNA-binding Proteins using Discrete Wavelet Transform and Optimized Integrated Features Space [J]. Chemometrics and Intelligent Laboratory Systems. 2018, 182: 21-30.

  6. Ming Zhang, Yan Xu, Lei Li, Zi Liu, Xibei Yang, Dong-Jun Yu*. Accurate RNA 5-methylcytosine Site Prediction Based on Heuristic Physical-Chemical Properties Reduction and Classifier Ensemble [J]. Analytical Biochemistry, 2018, 550: 41-48.

  7. Jun Hu, Yang Li, Yang Zhang *, and Dong-Jun Yu*. ATPbind: accurate protein-ATP binding site prediction by combining sequence-profiling and structure-based comparisons [J]. Journal of Chemical Information and Modeling. 2018, 58 (2): 501-510.

  8. Muhammad Kabir, Saeed Ahmed, Muhammad Iqbal, Zar Nawab Khan Swati, Liu Zi, and Dong-Jun Yu*. Improving prediction of extracellular matrix proteins using evolutionary information via a grey system model and asymmetric under-sampling technique [J]. Chemometrics and Intelligent Laboratory Systems. 2018, 174: 22-32.

  9. Chun-Qiu Xia, Ke Han, Yong Qi, Yang Zhang, and Dong-Jun Yu*. A Self-Training Subspace Clustering Algorithm under Low-Rank Representation for Cancer Classification on Gene Expression Data [J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2018, 15 (4): 1315-1324.

  10. Yan He, Ye Qiaolin, Zhang Tianan, Yu Dong-Jun et al. Least squares twin bounded support vector machines based on L1-norm distance metric for classification [J]. Pattern Recognition,  2018, 74: 434-447.

  11. Yan He, Ye Qiaolin, Zhang Tianan, Yu Dong-Jun et al. L1-Norm GEPSVM Classifier Based on an Effective Iterative Algorithm for Classification [J]. Neural Processing Letters, 2018, 48: 273-298.

  12. Jingzheng Li, Xibei Yang, Xiaoning Song, Jinghai Li, Pingxin Wang, and Dong-Jun Yu. Neighborhood attribute reduction: A multi-criterion approach. International Journal of Machine Learning and Cybernetics. 2018 (In press)

  13. He Yan, Qiaolin Ye, Tian’an Zhang, and Dong-Jun Yu*. Efficient and robust TWSVM classifier based on L1-norm distance metric for pattern classification. The 4th Asian Conference on Pattern Recognition (ACPR 2017). 2017: 436-441

  14. Jun Hu, Zi Liu, and Dong-Jun Yu*. Enhancing Protein-ATP and Protein-ADP Binding Sites Prediction Using Supervised Instance-Transfer Learning. The 4th Asian Conference on Pattern Recognition (ACPR 2017). 2017: 759-763

  15. Jun Hu, Yang Li, Ming Zhang, Xibei Yang, Hong-Bin Shen, and Dong-Jun Yu*. Predicting Protein-DNA Binding Residues by Weightedly Combining Sequence-based Features and Boosting Multiple SVMs [J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2017, 14 (6): 1389-1398. 

  16. Muhammad Kabir, Dong-Jun Yu*.Predicting DNase I hypersensitive sites via un-biased pseudo trinucleotide composition [J]. Chemometrics and Intelligent Laboratory Systems. 2017, 167: 78-84.

  17. Guang-Qing Li, Yang Li, Hong-Bin Shen, and Dong-Jun Yu*. TargetM6A: Identifying N6-methyladenosine Sites from RNA Sequences via Position-specific Nucleotide Propensity and Support Vector Machine [J]. IEEE Transactions on NanoBioscience, 2016, 15 (7): 674-682. 

  18. Jun Hu, Ke Han, Yang Li, Xue He, Jing-Yu Yang, Hong-Bin Shen, and Dong-Jun Yu*. TargetCrys: Protein Crystallization Prediction by Fusing Multi-View Features with Two-Layered SVM [J]. Amino Acids, 2016, 48(11): 2533-2547

  19. Ming Zhang, Jia-Wei Sun, Zi Liu, Ming-Wu Ren, Hong-Bin Shen, and Dong-Jun Yu*. Improving m6A Sites Prediction with Heuristic Selection of Nucleotide Physical-chemical Properties [J]. Analytical Biochemistry, 2016, 508: 104-113.

  20. Hengrong Ju, Xibei Yang, Hualong Yu, Tongjun Li, Dong-Jun Yu, and Jingyu Yang. Cost-sensitive Rough Set Approach [J]. Information Sciences, 2016, 355-356: 282-298.

  21. Suping Xu, Xibei Yang, Hualong Yu, Dong-Jun Yu, Jing-Yu Yang, and Eric C.C. Tsangd. Multi-label learning with label-specific feature reduction [J]. Knowledge-based Systems, 2016, 104: 52-61.

  22. Zhi-Sen Wei, Ke Han, Jing-Yu Yang, Hong-Bin Shen, and Dong-Jun Yu*. Protein-Protein Interaction Sites Prediction by Ensembling SVM and Sample-weighted Random Forests [J]. Neurocomputing, 2016, 193: 201-212. [DOI: 10.1016/j.neucom.2016.02.022]

  23. Jun Hu, Yang Li, Jing-Yu Yang, Hong-Bin Shen, and Dong-Jun Yu*. GPCR-drug Interactions Prediction Using Random Forest with Drug-Association-Matrix-Based Post-Processing Procedure [J]. Computational Biology and Chemistry. 2016, 60: 59-71. [DOI: 10.1016/j.compbiolchem.2015.11.007]

  24. Jun Hu, Yang Li, Wu-Xia Yan, Jing-Yu Yang, Hong-Bin Shen, and Dong-Jun Yu*. KNN-based Dynamic Query-Driven Sample Rescaling Strategy for Class Imbalance Learning [J].Neurocomputing, 2016, 191: 363–373. [DOI: 10.1016/j.neucom.2016.01.043]

  25. Zi Liu, Xuan Xiao, Dong-Jun Yu, Jianhua Jia, Wang-Ren Qiu, Kuo-Chen Chou.pRNAm-PC: Predicting N6-methyladenosine sites in RNA sequences via physicalchemical properties [J]. Analytical Biochemistry. 2016, 497: 60-67 [DOI:  10.1016/j.ab.2015.12.017]

  26. Guang-Hui Liu, Hong-Bin Shen, and Dong-Jun Yu*. Prediction of Protein-Protein Interaction Sites with Machine Learning based Data-Cleaning and Post-Filtering Procedures. Journal of Membrane Biology. 2016, 249 (1): 141-153. [DOI: 10.1007/s00232-015-9856-z]

  27. Zhi-Sen Wei, Jing-Yu Yang, Hong-Bin Shen, and Dong-Jun Yu*. A Cascade Random Forests Algorithm for Predicting Protein-Protein Interaction Sites [J]. IEEE Transactions on NanoBioscience. 2015, 14 (7): 746-760. [DOI: 10.1109/TNB.2015.2475359]

  28. Xue He, Ke Han, Jun Hu, Hui Yan, Jing-Yu Yang, Hong-Bin Shen, and Dong-Jun Yu*. TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition [J]. Journal of Membrane Biology. 2015, 248 (6): 1005-1014. [DOI: 10.1007/s00232-015-9811-z]

  29. Dong-Jun Yu, Yang Li, Jun Hu, Xibei Yang, Jing-Yu Yang, and Hong-Bin Shen. Disulfide Connectivity Prediction Based on Modelled Protein 3D Structural Information and Random Forest Regression [J], IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2015, 12 (3): 611-621. [DOI: 10.1109/TCBB.2014.2359451]

  30. Dong-Jun Yu, Jun Hu, Qian-Mu Li, Zhen-Min Tang, Jing-Yu Yang, and Hong-Bin Shen. Constructing Query-Driven Dynamic Machine Learning Model with Application to Protein-Ligand Binding Sites Prediction [J], IEEE Transactions on NanoBioscience.  2015, 14 (1): 45-58. [DOI: 10.1109/TNB.2015.2394328]

  31. Xibei Yang, Yong Qi, Dong-Jun Yu, Hualong Yu, Jing-Yu Yang. α-Dominance Relation and Rough Sets in Interval-valued Information System [J],Information Sciences.  2015, 294: 334-347. [DOI: 10.1016/j.ins.2014.10.003]

  32. Jun Hu, Xue He, Dong-Jun Yu*, Xi-Bei Yang, Jing-Yu Yang, and Hong-Bin Shen. A New Supervised Over-Sampling Algorithm with Application to Protein-Nucleotide Binding Residues Prediction [J], PLoS ONE. 2014, 9 (9): e107676. [DOI: 10.1371/journal.pone.0107676]

  33. Dong-Jun Yu, Jun Hu, Hui Yan, Xi-Bei Yang, Jing-Yu Yang, and Hong-Bin Shen. Enhancing Protein-Vitamin Binding Residues Prediction by Multiple Heterogeneous Subspace SVMs Ensemble [J], BMC Bioinformatics. 2014, 15:297. [DOI: 10.1186/1471-2105-15-297]

  34. Dong-Jun Yu,  Jun Hu, Jing Yang, Hong-Bin Shen, Jinhui Tang, and Jing-Yu Yang. Designing Template-Free Predictor for Targeting Protein-Ligand Binding Sites with Classifier Ensemble and Spatial Clustering [J].IEEE/ACM Transactions on Computational Biology and Bioinformatics.  2013, 10 (4): 994-1008.  [DOI: 10.1109/TCBB.2013.104]

  35. Dong-Jun Yu, Jun Hu, Yan Huang, Hong-Bin Shen, Yong Qi, Zhen-Min Tang and Jing-Yu Yang. TargetATPsite: A Template-free Method for ATP Binding Sites Prediction with Residue Evolution Image Sparse Representation and Classifier Ensemble [J], Journal of Computational Chemistry.  2013, 34 (11):974-985.  (Published as Inside Cover Story) [DOI: 10.1002/jcc.23219]

  36. Dong-Jun Yu, Jun Hu, Xiao-Wei Wu, Hong-Bin Shen, Jun Chen, Zhen-Min Tang, Jian Yang, and Jing-Yu Yang. Learning Protein Multi-View Features in Complex Space [J], Amino Acids. 2013, 44(5):1365-1379. [DOI: 10.1007/s00726-013-1472-6]

  37. Dong-Jun Yu, Jun Hu, Zhen-Min Tang, Hong-Bin Shen, Jian Yang, and Jing-Yu Yang. Improving Protein-ATP Binding Residues Prediction by Boosting SVMs with Random Under-Sampling [J]. Neurocomputing. 2013, 104: 180-190. [DOI: doi:10.1016/j.neucom.2012.10.012]

  38. Dong-Jun Yu, Xiao-Wei Wu, Hong-Bin Shen, Jian Yang, Zhen-Min Tang, Yong Qi, and Jing-Yu Yang.Enhancing Membrane Protein Subcellular Localization Prediction by Parallel Fusion of Multi-View Features [J]. IEEE Transactions on NanoBioscience. 2012,11 (4):375-385. [DOI: 10.1109/TNB.2012.2208473]

  39. Dong-Jun Yu, Hong-Bin Shen and Jing-Yu Yang. SOMPNN: An Efficient Non-Parametric Model for Predicting Transmembrane Helices [J]. Amino Acids. 2012, 42 (6):2195-2205. [DOI: 10.1007/s00726-011-0959-2]

  40. Ya-Nan Zhang+, Dong-Jun Yu+, Shu-Sen Li, Yong-Xian Fan, Yan Huang, and Hong-Bin Shen. Predicting Protein-ATP Binding Sites from Primary Sequence through Fusing Bi-Profile Sampling of Multi-View Features [J]. BMC Bioinformatics, 2012, 13 (1): 118. [DOI: 10.1186/1471-2105-13-118]

  41. Dong-Jun Yu, Hong-Bin Shen and Jing-Yu Yang. SOMRuler: A Novel Interpretable Transmembrane Helices Predictor [J]. IEEE Transactions on NanoBioscience. 2011,10 (2):121-129. [DOI: 10.1109/TNB.2011.2160730]

  42. Xi-Bei Yang, Dong-Jun Yu, Jing-Yu Yang, Li-Hua Wei. Dominance-based Rough Set Approach to Incomplete Interval-valued Information System [J]. Data & Knowledge Engineering, 2009, 68 (11):1331-1347.

  43. Xi-Bei Yang, Tsau Young Lin, Jing-Yu Yang, Yan Li, Dong-Jun Yu. Combination of interval-valued fuzzy set and soft set [J]. Computers & Mathematics with Applications, 2009, 58 (3):521-527.

  44. Xi-Bei Yang, Dong-Jun Yu, Jing-Yu Yang, Xiao-Ning Song. Difference Relation-based Rough Set and Negative Rules in Incomplete Information System [J]. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2009, 17 (5):649-665.

  45. Xi-Bei Yang, Jing-Yu Yang, Chen Wu, Dong-Jun Yu. Dominance-based Rough Set Approach and Knowledge Reductions in Incomplete Ordered Information System [J]. Information Sciences, 2008, 178 (4):1219-1234.

  46. Dong-Jun Yu, Hai-Tao Zhao, and Jing-Yu Yang. Face Recognition: An Approach Based on Feature Fusion and Neural Network [J], Acta Simulata Systematica Sinica, 2005, 17(5):1179-1182.

  47. Yong Xu, Jing-Yu Yang, Jian-Feng Lu,Dong-Jun Yu. An Efficient Renovation on Kernel Fisher Discriminant Analysis and Face Recognition Experiments [J]. Pattern Recognition, 2004, 37 (10):2091-2094.

  48. Dong-Jun Yu, Hai-Tao Zhao, and Jing-Yu Yang. A Fuzzy Neural Model for Face Recognition [J]. Acta Simulata Systematica Sinica, 2003, 15(2): 257-261.

  49. Shi-Tong Wang, Dong-Jun Yu, Jing-Yu Yang. Integrating Rough Set Theory and Fuzzy Neural Network to Discover Fuzzy Rules [J]. Intelligent Data Analysis, 2003, 7(1): 59-73.

  50. Zhi-Sen Wei, Jing-Yu Yang, andDong-Jun Yu*. Predicting Protein-Protein Interactions with Weighted PSSM Histogram and Random Forests [C]. 2015 Sino-foreign-interchange Workshop on Intelligence Science and Big Data Engineering (IScIDE 2015)LNCS, Volume 9242, pp.  326-335. Springer, Heidelberg .

  51. Dong-Jun Yu, Jun Hu, Jian-Hua Xie, Yong Qi, and Zhen-Min Tang. Supervised Kernel Self-Organizing Map [C]. 2012 Sino-foreign-interchange Workshop on Intelligence Science and Intelligent Data Engineering (IScIDE 2012).Lecture Notes in Computer Science (LNCS), Volume 7751, pp. 246-253. Springer, Heidelberg (2013).

  52. Dong-Jun Yu, Xiao-Wei Wu, and Wei-Wei Yang. Gender Determination from Single Facial Image by Utilizing Surface Shape Information [C]. Communications in Computer and Information Science, Vol. 288:696-705. Springer, Heidelberg, 2012.

  53. Dong-Jun Yu, E. R. Hancock, W. A. P. Smith. A Riemannian Self-organizing Map [C]. The 15th International Conference on Image Analysis and Processing, Springer Verlag, Lecture Notes in Computer Science (LNCS), Volume 5716: 229-238, 2009.

  54. Dong-Jun Yu, Jian-Feng Lu, Jing-Yu Yang. Geodesic Discriminant Analysis on Curved Riemannian Manifold [C]. The Sixth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), IEEE Computer Society, vol. 5: 379-383, 2009.

  55. Dong-Jun Yu,  E. R. Hancock, W. A. P. Smith. Learning a Self-Organizing Map Model on a Riemannian Manifold [C]. Proceeding of Thirteenth IMA Conference on the Mathematics of Surfaces, Springer Verlag, Lecture Notes in Computer Science (LNCS), Volume 5654: 375-390, 2009.

  56. Dong-Jun Yu, Xiao-Jun Wu, Jing-Yu Yang. Quantitative Measurement for Fuzzy System to Input and Rule Perturbations [C], Lecture Notes in Computer Science (LNCS), Volume 4114: pp 159-164, 2006.

  57. Dong-Jun Yu, Yong-Hong Xu, Xiao-Jun Wu, and Jing-Yu Yang. Statistical Quantitative Sensitivity Measurement for Fuzzy System [J], Acta Simulata Systematica Sinica, 2006, 18(9): 2433-2437.

  58. Dong-Jun Yu, Yong Qi, Yong-Hong Xu, Jing-Yu Yang. Kernel-SOM Based Visualization of Financial Time Series Forecasting [C]. International Conference on Innovative Computing, Information and Control (ICIC), Beijing, China, 2006, pp: 470-473.

  59. 於东军, 吴小俊, E. R. Hancock, 杨静宇. 广义SOM及其在人脸性别识别中的应用[J]. 计算机学报. 2011, 34(9):1719-1725.

  60. 於东军, 郑宇杰, 吴小俊, 杨静宇. 基于Kernel-SOM的非线性系统辨识及模型运行收敛性分析[J]. 电子与信息学报. 2008, 30(8): 1928-1931.

  61. 郑宇杰, 杨静宇, 徐勇, 於东军. 一种基于Fisher鉴别极小准则的特征提取方法[J]. 计算机研究与发展, 2006, 43(7): 1201-1206.

  62. 郑宇杰, 杨静宇, 吴小俊, 於东军. 基于对称 ICA 的特征抽取方法及其在人脸识别中的应用[J]. 模式识别与人工智能, 2006, 19(1): 116-121.

  63. 於东军, 王士同, 杨静宇. 一种增量式规则提取算法[J]. 小型微型计算机系统. 2004, 25(1): 79-81.

  64. 於东军, 徐蔚鸿, 赵海涛, 杨静宇. 基于神经网络的人脸自动识别[J]. 电子与信息学报. 2003, 25(9): 1160-1167.

  65. 赵海涛, 於东军, 金忠, 杨静宇. 基于形状和纹理的人脸自动识别[J]. 计算机研究与发展, 2003, 43(1): 538-543.

  66. 王士同, 於东军. 非线性系统的模糊辨识误差分析(英文)[J].软件学报. 2000, 11(4): 447-452.

  67. 於东军, 王士同. B样条神经网络的构造理论[J]. 计算机研究与发展. 1999, 36(5): 534-540.

  68. 於东军, 王士同. 层次径向基神经网络的全局逼近理论[J]. 计算机研究与发展. 1999, 36(11): 1329-1334.

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