目录
关闭
硕士报考志愿采集    更新日期：2020年1月6日
姓 名 於东军 性 别 男
出生年月 1975年10月 籍贯
民 族 汉族 政治面貌 中国共产党党员
最后学历 研究生毕业 最后学位 工学博士
技术职称 教授 导师类别 博士生导师
导师类型 校内 兼职导师 否
行政职务 Email njyudj@njust.edu.cn
工作单位 南京理工大学计算机科学与工程学院 邮政编码 210094
通讯地址 江苏南京孝陵卫200号
单位电话 025-84315751-3079
个人主页 http://csbio.njust.edu.cn
指导学科
学科专业(主) 081104|模式识别与智能系统 招生类别 博、硕士 所在学院 计算机科学与工程学院
研究方向

1. 模式识别

2. 机器学习

3. 生物信息学

更多访问: http://csbio.njust.edu.cn
学科专业（辅） 0812|计算机科学与技术 招生类别 博、硕士 所在学院 计算机科学与工程学院
研究方向

1. 计算机应用

2. 人工智能

3. 生物计算

更多访问: http://csbio.njust.edu.cn
学科专业（辅） 0831|生物医学工程 招生类别 硕士 所在学院 计算机科学与工程学院
研究方向
工作经历

於东军, 男, 工学博士, 南京理工大学计算机科学与工程学院智能科学与技术系教授, 博士生导师。主要研究方向为生物信息计算、机器学习、模式识别与智能系统。主持及参与多项国家自然科学基金及省部级项目，在模式识别、生物信息学、神经网络、智能计算等领域有一定的研究积累，累计发表学术论文80余篇，主编21世纪高校应用型规划教材《Java程序设计与应用开发》(2009)。近年来主要从事生物信息及模式识别领域相关的课题研究，相关成果已发表在Bioinformatics、Journal of Chemical Information and Modeling、Pattern Recognition、Machine Learning、IEEE/ACM Transactions on Computational Biology and Bioinformatics、IEEE Transactions on NanoBioscience、Journal of Computational Chemistry、BMC Bioinformatics、Amino Acids、计算机学报、计算机研究与发展、软件学报等国内外刊物上。曾获江苏省科技进步三等奖(2001)、江苏省优秀硕士论文(2002)、南京理工大学董事会奖教金一等奖 (2007)、教育部科技进步(推广类)二等奖(2009)以及教育部科技进步二等奖(2012)。入选江苏省博士集聚计划(2013)、江苏省“333高层次人才培养工程”中青年科学技术带头人(2013)、江苏省“六大人才高峰”(2013)以及南京理工大学“科技工作先进个人”(2014)。英国The University of York计算机系访问学者(2008)，美国The University of Michigan (Ann Arbor)计算医学与生物信息学系访问学者(2016)。

获奖、荣誉称号

江苏省科技进步三等奖(2001)

江苏省优秀硕士论文(2002)

南京理工大学董事会奖教金一等奖 (2007)

教育部科技进步(推广类)二等奖(2009)

教育部科技进步二等奖(2012)

江苏省博士集聚计划(2013)

江苏省“333高层次人才培养工程”中青年科学技术带头人(2013)

江苏省“六大人才高峰”(2013)

南京理工大学“科技工作先进个人”(2014)

英国The University of York计算机系访问学者(2008)

美国The University of Michigan (Ann Arbor)计算医学与生物信息学系访问学者(2016)。

社会、学会及学术兼职
      中国计算机学会(CCF)生物信息学专业委员会委员
      中国人工智能学会(CAAI)生物信息学与人工生命专业委员会委员
      中国人工智能学会(CAAI)第七届理事会会员
      江苏省计算机学会高级会员
      中国计算机学会(CCF)高级会员
      中国人工智能学会(CAAI)高级会员
      国际电气和电子工程师协会(IEEE)会员

 

科研项目
      国家自然科学基金-面上项目 大规模多源异质蛋白质数据挖掘中的若干关键问题研究 (No.61772273)   2018.01-2021.12
      中央高校基本科研业务费专项资金 面向海量生物数据的挖掘模型与方法研究 (No. 30916011327) 2016.01-2017.12
      国家自然科学基金-面上项目 蛋白质-配体绑定区域预测的特征抽取及学习算法研究 (No. 61373062) 2014.01-2017.12
      江苏省自然科学基金-面上项目 面向蛋白质生物计算的特征抽取及动态学习模型研究 (No. BK20141403) 2014.07-2017.06
      江苏省“博士集聚计划”项目 信息化多层次呼吸系统慢性病诊疗与实时监管 2013.10-2015.09
      江苏省“六大人才高峰”项目 蛋白质结构与功能预测中的几个关键问题研究 (No. 2013-XXRJ-022) 2013.08-2016.07
      江苏省产学研联合创新资金 城市光伏电站发电能力建模预测与运行控制研究 (No. BY2012022) 2012.07-2014.06
      江苏省自然科学基金-面上项目 融合形变模型与辐照约束机制的人脸三维形状恢复研究 (No. BK2011371) 2011.08-2013.12
      江苏省科技支撑计划-工业部分 基于三维仿真技术的精度造船数据实时过程管理系统 (No. BE2011158) 2010.05-2013.05
      江苏省科技支撑计划-工业部分 大规模分布式光伏电站组实时远程智能运行控制系统 (No. BE2010166) 2010.05-2012.12
      中国博士后特别资助 基于动态学习框架的蛋白质二硫键预测 (No. 2014T70526) 2014.07-2015.06
      中国博士后基金一等资助 蛋白质计算中多视角特征抽取及可解释学习算法研究 (No. 2013M530260) 2013.01-2014.12
      中央高校基本科研业务费专项资金 蛋白质结构与功能预测研究 (No. 30920130111010) 2013.01-2014.12
      中央高校基本科研业务费专项资金 人脸二维图象的三维形状恢复研究 (No. 2011YDXM19) 2011.01-2012.12

 

发表论文
  1. 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.

  2. Yang Li, Jun Hu, Chengxin Zhang, Dong-Jun Yu*, and Yang Zhang*. ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks [J]. Bioinformatics, 2019, In Press.

  3. 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, 2019, In Press.

  4. Yang Li, Chengxin Zhang, Eric W. Bell, Dong-Jun Yu*, Yang Zhang*, Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13, Proteins, 2019, In Press.

  5. 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, In Press.

  6. 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 & High Throughput Screening, 2019, In Press.

  7. 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, In Press.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  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. 2019, 10 (4): 731-742.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  21. 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.

  22. 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.

  23. 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

  24. 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

  25. 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. 

  26. 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.

  27. 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. 

  28. 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

  29. 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.

  30. 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.

  31. 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.

  32. 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]

  33. 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]

  34. 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]

  35. 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]

  36. 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]

  37. 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]

  38. 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]

  39. 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]

  40. 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]

  41. 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]

  42. 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]

  43. 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]

  44. 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]

  45. 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]

  46. 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]

  47. 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]

  48. 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]

  49. 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]

  50. 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]

  51. 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]

  52. 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.

  53. 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.

  54. 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.

  55. 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.

  56. 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.

  57. 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.

  58. 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.

  59. 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.

  60. 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 .

  61. 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).

  62. 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.

  63. 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.

  64. 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.

  65. 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.

  66. 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.

  67. 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.

  68. 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.

  69. 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.

  70. 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.

  71. 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.

  72. 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.

  73. 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.

  74. 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.

  75. 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.

  76. 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.

  77. 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.

  78. 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.

  79. 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.

  80. 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.

  81. 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.