Publications


  1. He Yan, Yong Qi, Qiaolin Ye, and Dong-Jun Yu*. Robust least squares twin support vector regression with adaptive FOA and PSO for short-term traffic flow prediction [J], IEEE Transactions on Intelligent Transportation Systems, 2021, In Press. 

  2. Fang Ge, Ying Zhang, Jian Xu, Arif Muhammad, Jiangning Song*, and Dong-Jun Yu*. Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion [J], Briefings in Bioinformatics, 2021, In Press.

  3. Fang Ge, Yi-Heng Zhu, Jian Xu, Arif Muhammad, Jiangning Song*, and Dong-Jun Yu*. MutTMPredictor: robust and accurate cascade XGBoost classifier for prediction of disease-associated mutations in transmembrane proteins [J], Computational and Structural Biotechnology Journal, 2021, In Press.

  4. Muhammad Arif, Saeed Ahmed, Fang Ge, Muhammad Kabir*, Yaser Daanial Khan, Dong-Jun Yu*, Maha Thafar*. Prediction of anticancer peptides by integrating optimized multiple feature descriptors with stacked ensemble approach [J], Chemometrics and Itelligent Laboratory Systems, 2022, 220: 104458.

  5. Jun Hu, Yan-Song Bai, Lin-Lin Zheng, Ning-Xin Jia, Dong-Jun Yu*, and Gui-Jun Zhang*. Protein-DNA Binding Residue Prediction via Bagging Strategy and Sequence-based Cube-Format Feature [J], IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021, In Press.

  6. He Yan, Tian’an Zhang, Yong Qi, and Dong-Jun Yu*. Short-term traffic flow prediction based on hybrid optimization algorithm [J], Applied Mathematical Modelling, 2022, Vol. 102: 385-404.

  7. Ke Han#, Long-Chen Shen#, Yi-Heng Zhu, Jian Xu, Jiangning Song*, and Dong-Jun Yu*. MAResNet: predicting transcript factor binding sites by combining multi-scale bottom-up and top-down attention and residual network [J], Briefings in Bioinformatics, 2021, In Press.

  8. Liang Rao, Ningxin Jia, Jun Hu*, Dong-Jun Yu*, and Guijun Zhang*. ATPdock: a template-based mehtod for ATP-specific protein-ligand docking [J], Bioinformactics, 2021, In Press.

  9. Ying Zhang, Yan Liu, Jian Xu, Xiaoyu Wang, Xinxin Peng, Jiangning Song*, and Dong-Jun Yu*. Leveraging the attention mechanism to improve the identification of DNA N6-methyladenine sites [J], Briefings in Bioinformatics, 2021, In Press.

  10. Yang Li, Chengxin Zhang, Wei Zheng, Xiaogen Zhou, Eric W. Bell, Dong-Jun Yu*, Yang Zhang*. Protein inter-residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14 [J], Proteins: Structure, Function, and Bioinformatics, 2021, In Press. [TripletRes - the second best predictor in RR group in CASP 14]

  11. Muhammad Arif, Muhammad Kabir, Saeed Ahmad, Fang Ge, Adel Khelifi, Dong-Jun Yu*. DeepCPPred: a deep learning framework for the discrimination of cell-penetrating peptides and their uptake efficiencies [J], IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021, In Press.

  12. Matee Ullah, Ke Han, Fazal Hadi, Jian Xu, Jiangning Song*, and Dong-Jun Yu*. PScL-HDeep: image-based prediction of protein subcellular location in human tissue using ensemble learning of handcrafted and deep learned features with two-layer feature selection [J]. Briefings in Bioinformatics, 2021, In Press.

  13. 华阳, 李金星, 冯振华, 宋晓宁*, 孙俊, 於东军*. 基于注意力机制与特征融合的蛋白质-药物相互作用预测[J], 计算机研究与发展, 2021. 已录用

  14. Yan Liu#, Ke Han#, Yi-Heng Zhu, Ying Zhang, Long-Chen Shen, Jiangning Song*, and Dong-Jun Yu*. Improving protein fold recognition using triplet network and ensemble deep learning [J]. Briefings in Bioinformatics, 2021, In Press.

  15. Yang Li, Chengxin Zhang, Eric Bell, Wei Zheng, Xiaogen Zhou, Dong-Jun Yu*, Yang Zhang*. Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks [J], PLoS Computational Biology. 2021, 17 (3): e1008865. [TripletRes - the best predictor in RR group in CASP 13]

  16. Long-Chen Shen, Yan Liu, Jiangning Song*, and Dong-Jun Yu*. SAResNet: self-attention residual network for predicting DNA-protein binding [J], Briefings in Bioinformatics, 2021, 22 (5): bbab101.

  17. Jing Xu, Fuyi Li, André Leier, Dongxu Xiang, Hsin-Hui Shen, Tatiana T. Marquez Lago, Jian Li, Dong-Jun Yu*, and Jiangning Song*. Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides [J], Briefings in Bioinformatics, 2021, 22 (5): bbab083.

  18. Yan Liu, Yi-Heng Zhu, Xiaoning Song, Jiangning Song*, Dong-Jun Yu*. Why can deep convolutional neural networks improve protein fold recognition? A visual explanation by interpretation [J]. Briefings in Bioinformatics, 2021, 22 (5): bbab001.

  19. Yi-Heng Zhu, Jun Hu, Fang Ge, Fuyi Li, Jiangning Song*, Yang Zhang*, Dong-Jun Yu*. Accurate Multi-Stage Prediction of Protein Crystallization Propensity Using Deep-Cascade Forest with Sequence-Based Features [J]. Briefings in Bioinformatics, 2021, 22 (3): bbaa076.

  20. Jun Hu*, Lin-Lin Zheng, Yan-Song Bai, Ke-Wen Zhang, Dong-Jun Yu*, and Gui-Jun Zhang*. Accurate prediction of protein-ATP binding residues solely using sequence information [J]. Analytical Biochemistry. 2021, Vol. 626: 114241.

  21. 葛芳, 胡俊, 朱一亨, 於东军*. 非同义单核苷酸变异致病性预测研究综述[J]. 南京理工大学学报:自然科学版, 2021, 45 (1): 1-17

  22. Fang Ge, Arif Muhammad, Dong-Jun Yu*. DeepnsSNPs: Accurate Prediction of Non-synonymous Single-nucleotide Polymorphisms by Combining Multi-scale Convolutional Neural Network and Residue Environment Information [J]. Chemometrics and Intelligent Laboratory Systems, 2021, 215: 104326.

  23. Jun Hu*, Liang Rao, Yi-Heng Zhu, Gui-Jun Zhang* and Dong-Jun Yu*. TargetDBP+: Enhancing the Performance of Identifying DNA-Binding Protein via Weightedly Combining Sequence-Based Convolutional Features [J]. Journal of Chemical Information and Modeling, 2021, 61(1): 505-515.

  24. Xiaoning Song, Yao Chen, Zhenhua Feng, Guosheng Hu, Dong-Jun Yu, Xiaojun Wu. SP-GAN: Self-growing and Pruning Generative Adversarial Networks [J], IEEE Transactions on Neural Networks and Learning Systems, 2021, 32 (6): 2458-2469.

  25. Saeed Ahmed, Muhammad Kabir, Muhammad Arif, Zaheer Ullah Khan, Dong-Jun Yu*. DeepPPSite: a deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence information [J], Analytical Biochemistry, 2021, 612: 113955.

  26. Fang Ge, Jun Hu, Yi-Heng Zhu, Muhammad Arif, Wen-Wen Kan, and Dong-Jun Yu*. TargetMM: Accurate Missense Mutation Prediction by Utilizing Local and Global Sequence Information with Classifier Ensemble [J]. Combinatorial Chemistry & High Throughput Screening, 2020, In Press.

  27. Muhammad Arif, Saeed Ahmad, Farman Ali, Fang Ge, Min Li, and Dong-Jun Yu*. TargetCPP: Accurate prediction of cell-penetrating peptides from optimized multi-scale feature using gradient boost decision tree [J]. Journal of Computer-Aided Molecular Design, 2020, 34 (8): 841-856.

  28. Farman Ali, Muhammad Arif, Zaheer Ullah Khan, Muhammad Kabir, Saeed Ahmed, and Dong-Jun Yu*. SDBP-Pred: Prediction of single-stranded and double-stranded DNA-binding proteins by extending consensus sequence and K-segmentation strategies into PSSM [J], Analytical Biochemistry, 2020, 589: 113494.

  29. Ming-Cai Chen, Yang Li, Yi-Heng Zhu, Fang Ge, Dong-Jun Yu*. SSCpred: Single-Sequence-Based Protein Contact Prediction Using Deep Fully Convolutional Network [J]. Journal of Chemical Information and Modeling, 2020, 60 (6): 3295−3303.

  30. Muhammad Arif, Saeed Ahmad, Farman Ali, Fang Ge, Min Li, Dong-Jun Yu*. TargetCPP: Accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree [J]. Journal of Computer-Aided Molecular Design, 2020, 34:841–856

  31. Yanchao Li, Yong li Wang, Dong-Jun Yu, Ye Ning, Peng Hu, and Ruxin Zhao. ASCENT: Active Supervision for Semi-supervised Learning [J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 32 (5): 868-882.

  32. Yi-Heng Zhu, Jun Hu, Xiaoning Song, and Dong-Jun Yu*. DNAPred: Accurate Identification of DNA-Binding Sites from Protein Sequence by Ensembling Hyperplane-Distance-based Support Vector Machines J]. Journal of Chemical Information and Modeling, 2019, 59 (6): 3057-3071.

  33. Yang Li, Jun Hu, Chengxin Zhang, and Dong-Jun Yu*, andYang Zhang*.ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks [J]. Bioinformatics, 2019, 35 (22): 4647-4655.

  34. Yang Li, Chengxin Zhang, Eric W. Bell, Dong-Jun Yu*, and Yang Zhang*, Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13 [J]. Proteins: Structure, Function, and Bioinformatics, 2019, 87 (12): 1082-1091. [TripletRes - the best predictor in RR group in CASP 13, Invited paper]

  35. Jun Hu, Xiao-Gen Zhou, Yi-Heng Zhu, Dong-Jun Yu*, and Gui-Jun Zhang*. TargetDBP: Accurate DNA-Binding Protein Prediction via Sequence-based Multi-View Feature Learning [J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, 17 (4): 1419-1429..

  36. 张寓, 於东军*. 基于一维卷积神经网络的蛋白质-ATP绑定位点预测[J]. 计算机应用, 2019, 39 (11): 3146-3150.

  37. Yi-Heng Zhu, Jun Hu, Yong Qi, and Dong-Jun Yu*. Boosting Granular Support Vector Machines for the Accurate Prediction of Protein-Nucleotide Binding Sites. Combinatorial Chemistry and High Throughput Screening, 2019, 22: 455-469.

  38. Xiao-Rong Bao, Yi-Heng Zhua, and Dong-Jun Yu*. DeepTF: Accurate Prediction of Transcription Factor Binding Sites from DNA Sequence by Combining Multi-Scale Convolutional Neural Network and Long Short-Term Memory Neural Network. IScIDE, 2019: 126-138.

  39. Xiaoning Song, Guosheng Hu, Jian-Hao Luo, Zhenhua Feng, Dong-Jun Yu, and Xiao-Jun Wu. Fast SRC using Quadratic Optimisation in Downsized Coefficient Solution Subspace [J]. Signal Processing, 2019, 161: 101-110.

  40. 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, 2019, 108 (6): 993-1018.

  41. 於东军, 李阳. 蛋白质残基接触图预测综述 [J]. 南京理工大学学报: 自然科学版, 2019, 43 (1): 1-12.

  42. 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, 2019, 564: 123-132.

  43. 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, 2019, 10 (4): 731-742.

  44. 於东军, 朱一亨, 胡俊. 识别蛋白质配体绑定残基的生物计算方法综述 [J]. 数据采集与处理, 2018, 33 (2): 195-206.

  45. 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, 34 (13): 2209-2218.

  46. 金康荣, 於东军*. 基于加权朴素贝叶斯分类器和极端随机树的蛋白质接触图预测 [J]. 南京航空航天大学学报, 2018, 50 (5): 619-628.

  47. 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, 182: 158-165.

  48. 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.

  49. 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.

  50. 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.

  51. 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.

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

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

  54. He Yan, Dong-Jun Yu*. Short-term traffic condition prediction of urban road network [J], International Smart Cities Conference (ISC2), IEEE, 2017: 1-2

  55. 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

  56. 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

  57. 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.

  58. 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.

  59. 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.

  60. 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

  61. 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.

  62. 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.

  63. 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.

  64. 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.

  65. 魏志森, 杨静宇, 於东军*. 基于加权 PSSM 直方图和随机森林集成的蛋白质交互作用位点预测 [J]. 南京理工大学学报: 自然科学版, 2015, 39 (4): 379-385.

  66. 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.

  67. 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.

  68. 郜法启, 於东军, 沈红斌. 基于分类器集成的跨膜蛋白两亲螺旋区域位置预测[J]. 南京理工大学学报: 自然科学版, 2016, (4): 431-437

  69. 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.

  70. 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.

  71. 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.

  72. 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.

  73. 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.

  74. 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.

  75. 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.

  76. 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.

  77. 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 Cover Story)

  78. 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.

  79. 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.

  80. 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.

  81. 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.

  82. 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.

  83. 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.

  84. 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.

  85. 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.

  86. 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.

  87. 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.

  88. 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.

  89. 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.

  90. 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.

  91. 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.

  92. 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 .

  93. 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).

  94. 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.

  95. 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), Vol. 5716: 229-238, 2009.

  96. 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.

  97. 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.

  98. 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), Vol. 4114: 159-164, 2006.

  99. 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.

  100. 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.

  101. 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.

  102. 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.

  103. 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.

  104. 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.

  105. 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.

  106. 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.

  107. 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.

  108. 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.

  109. 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.

  110. 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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