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 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 -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****(2008)，美国The University of Michigan (Ann Arbor)计算医学与生物信息学系访问****(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****(2008)
美国The University of Michigan (Ann Arbor)计算医学与生物信息学系访问****(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.**) 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. ) 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. **) 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. BK**) 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. BY**) 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. BK**) 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. BE**) 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. BE**) 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. 010) 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
Yi-Heng Zhu, Jun Hu, Xiaoning Song, andDong-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.
Yang Li, Jun Hu, Chengxin Zhang,Dong-Jun Yu*, andYang Zhang*.ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks [J]. Bioinformatics, 2019,In Press.

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.

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.

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.

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.

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.

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.

He Yan, Qiao-Lin Ye, andDong-Jun Yu*.Efficient and robust TWSVM classification via a minimum L1-norm distance metric criterion [J]. Machine Learning, 2019, 108 (6): 993–1018.

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.

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.

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.

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.

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.

Muhammad Kabir, Muhammad Arif, Saeed Ahmad, Zakir Ali, andDong-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.

Farman Ali, Muhammad Kabir, Muhammad Arif, Zar Nawab Khan Swati, Zaheer Ullah Khan, Matee Ullah, andDong-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.

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.

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.

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.

Chun-Qiu Xia, Ke Han, Yong Qi, Yang Zhang, andDong-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.

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.

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.

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

Jun Hu, Zi Liu, andDong-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

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.

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.

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.

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

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.

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.

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.

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]

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]

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]

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]

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]

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.**]

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]

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.**]

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.**]

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

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.**]

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]

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]

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]

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]

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]

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.**]

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]

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]

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.**]

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.

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.

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.

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.

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.

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.

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.

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.

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 .

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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