Publications


  1. Book Chapter: Yang Li, Yan Liu, and Dong-Jun Yu*Machine Learning for Protein Inter-Residue Interaction Prediction, Chapter 7 in  the book of "Machine Learning in Bioinformatics of Protein Sequences" [M]: 183-203, World Scientific Publishing Company, 2023.

  2. Hailong Yang, Yue Chen, Yun Zuo, Zhaohong Deng, Xiaoyong Pan, Hong-Bin Shen, Kup-Sze Choi, and Dong-Jun Yu. MINDG: A Drug-Target Interaction Prediction Method based on An Integrated Learning Algorithm [J]. Bioinformatics, 2024, In press.

  3. Jia-Shun Wu, Yan Liu, Fang Ge, and Dong-Jun Yu*. Prediction of Protein-ATP Binding Residues Using Multi-view Feature Learning via Contextual-based Co-attention Network [J]. Computer in Biology and Mdecicine, 2024, In press.

  4. Zi-Hao Yan, Fang Ge, Yan Liu, Yumeng Zhang, Fuyi Li, Jiangning Song, and Dong-Jun Yu*. TransEFVP: a two-stage approach for the prediction of human pathogenic variants based on protein sequence embedding fusion [J]. Journal of Chemical Information and Modeling, 2024, In press.

  5. Ming Zhang, Chao Gong, Fang Ge, Dong-Jun Yu*. FCMTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multi-Scale Convolution and Deep Feature Combination within a Transformer Framework [J]. Journal of Chemical Information and Modeling, 2024, In press.

  6. Yi-Heng Zhu, Zi Liu, Yan Liu, Zhiwei Ji*, and Dong-Jun Yu*. ULDNA: Integrating Unsupervised Multi-Source Language Models with LSTM-Attention Network for High-Accuracy Protein-DNA Binding Site Prediction [J]. Briefings in Bioinformatics, 2024, In press.

  7. Zi Liu, Chengxin Zhang, Qidi Zhang, Yang Zhang*, and Dong-Jun Yu*. TM-search: an efficient and effective tool for protein strucutre database search [J]. Journal of Chemical Information and Modeling, 2024, In press.

  8. Shuo Li, Yan Liu, Long-Chen Shen, He Yan, Jiangning Song*, and Dong-Jun Yu*. GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference [J]. Briefings in Bioinformatics, 2023, In press.

  9. He Yan, Yan Liu, Yanmeng Li, Qiaolin Ye, Dong-Jun Yu*, and Yong Qi*. Robust GEPSVM Classifier: An Efficient Iterative Optimization Framework [J]. Information Sciences, 2023, In Press.

  10. Ying Zhang, Zhikang Wang, Yiwen Zhang, Shanshan Li, Yuming Guo, Jiangning Song*, and Dong-Jun Yu*. Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues [J]. Bioinformatics, 2023, 39 (12): btad709.

  11. Zi Liu, Yi-Heng Zhu, Long-Chen Shen, Xuan Xiao, Wang-Ren, Dong-Jun Yu*. Integrating unsupervised language model with multi-view multiple sequence alignments for high-accuracy inter-chain contact prediction [J]. Computers in Biology and Medicine, 2023,  166: 107529.

  12. Qunzhou Wu, Zhaohong Deng*, Wei Zhang, Xiaoyong Pan, Kup-Sze Choi, Yun Zou, Hong-Bin Shen, Dong-Jun Yu. MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering [J]. Bioinformatics, 2023, 39 (8): btad499.

  13. Ying Zhang, Fang Ge, Fuyi Li, Xibei Yang, Jiangning Song*, Dong-Jun Yu*. Prediction of multiple types of RNA modifications via biological language model [J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023, 20 (5): 3205-3214.

  14. He Yan, Li Cheng, Qiaolin Ye, Dong-Jun Yu*, Yong Qi*. Robust Generalized Canonical Correlation Analysis [J]. Applied Intelligence, 2023, 53: 21140-21155.

  15.  Yan Liu, Guo Wei, Chen Li, Long-Cheng Shen, Robin B. Gasser, Jiangning Song*, Dijun Chen*, and Dong-Jun Yu*. TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level [J]. Briefings in Bioinformatics, 2023, 24 (3): bbad132.

  16. Jun Hu*, Wen-Wu Zeng, Ning-Xin Jia, Muhammad Arif, Dong-Jun Yu*, and Gui-Jun Zhang*. Improving DNA-Binding Protein Prediction Using Three-Part Sequence-Order Feature Extraction and a Deep Neural Network Algorithm [J]. Journal of Chemical Information and Modeling, 2023, 63 (3): 1044-1057. 

  17. Yi-Heng Zhu, Chengxin Zhang, Dong-Jun Yu*, Yang Zhang*. Integrating Unsupervised Language Model with Triplet Neural Networks for Protein Gene Ontology Prediction [J]. PLoS Computational Biology, 2022, 18 (12): e1010793.

  18. Fang Ge, Chen Li, Shahid Iqbal, Arif Muhammand, Fuyi Li, Maha A. Thafar, Zihao Yan, Apilak Worachartcheewan, Xiaofei Xu, Jiangning Song*, Dong-Jun Yu*. VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants [J]. Briefings in Bioinformatics, 2023, 24 (1): bbac535.

  19. Jing Ba, Pingxin Wang, Xibei Yang, Hualong Yu, and Dong-Jun Yu. GLEE: a granularity filter for feature selection [J]. Engineering Application of Artificial Intelligence, 2023, 122:106080.

  20. Yan Liu, He Yan, Long-Chen Shen, Dong-Jun Yu*. Learning Cell Annotation under Multiple Reference Datasets by Multi-source Domain Adaptation [J]. Journal of Chemical Information and Modeling, 2022, 63 (1): 397-405. 

  21. Peng-Hao Wang, Yi-Heng Zhu, Xibei Yang, Dong-Jun Yu*. GCmapCrys: Integrating graph attention network with predicted contact map for multi-stage protein crystallization propensity prediction [J]. Analytical Biochemistry, 2023, 663: 115020.

  22. Matee Ullah, Fazal Hadi, Jiangning Song*, Dong-Jun Yu*. PScL-2LSAESM: bioimage-based prediction of protein subcellular localization by integrating heterogenous features with the two-level SAE-SM and mean ensemble method [J]. Bioinformatics, 2023, 39 (1): btac727.

  23. Xue-Qiang Fan, Jun Hu*, Yu-Xuan Tang, Ning-Xin Jia, Dong-Jun Yu* and Gui-Jun Zhang*. Predictiong RNA solvent accessibility from multi-scale context feature via multi-shot neural network [J]. Analytical Biochemistry, 2022, Vol. 654: 114802.

  24. Yu-Hang Yin, Long-Chen Shen, Yuanhao Jiang, Shang Gao*, Jiangning Song*, and Dong-Jun Yu*. Improving the prediciton of DNA-protein binding by integrating multi-scale dense convolutional network with fault-tolerant coding [J]. Analytical Biochemistry, 2022, Vol. 656: 114878.

  25. Guan-Yu Zhu, Yan Liu, Peng-Hao Wang, Xibei Yang, Dong-Jun Yu*. Learning Protein Embedding to Improve Protein Fold Recognition Using Deep Metric Learning [J]. Journal of Chemical Information and Modeling, 2022, 62 (17): 4283-4291.

  26. Shahid Iqbal, Fang Ge, Fuyi Li, Tatsuya Akutsu, Yuanting Zheng, Robin B. Gasser, Dong-Jun Yu*, Geoffery Webb*, and Jiangning Song*.  PROST: AlphaFold2-aware sequence-based predictor to estimate protein stability changes upon missense mutations [J]. Journal of Chemical Information and Modeling, 2022, 62 (17): 4270-4282.

  27. Qunzhuo Wu, Zhaohong Deng, Xiaoyong Pan, Hong-Bin Shen, Kup-Sze Choi, Shitong Wang, Jing Wu, and Dong-Jun Yu. MDGF-MCEC: A multi-view dual attention embedding model with cooperative ensemble learning for CircRNA-disease association prediction [J]. Briefings in Bioinformatics, 2022, 23 (5): bbac289.

  28. Matee Ullah, Fazal Hadi, Jiangning Song*, and Dong-Jun Yu*. PScL-DDCFPred: an ensemble deep learning-based approach for characterizing multiclass subcellular localization of human proteins from bioimage data [J]. Bioinformatics, 2022, 38 (16): 4019-4026. 

  29. He Yan, Liyong Fu, Yong Qi, Dong-Jun Yu, Qiaolin Ye. Robust ensemble method for short-term traffic flow prediction based on a hybrid optimization algorithm [J]. Future Generation Computer Systems, 2022, 133:395-410.

  30. Yang Li, Chengxin Zhang, Dong-Jun Yu*, Yang Zhang*. Deep learning geometrical potential for high-accuracy ab initio protein structure prediction [J]. iScience, 2022: 104425.

  31. He Yan, Liyong Fu, Tian'an Zhang, Jun Hu, Qiaolin Ye, Yong Qi, and Dong-Jun Yu*. Robust distance metric optimization driven GEPSVM classifier for pattern classification [J], Pattern Recognition, 2022, 129: 108779. 

  32. Ke Han, Yan Liu, Jian Xu, Jiangning Song*, and Dong-Jun Yu*. Performing protein fold recognition by exploiting a stack convolutional neural network with the attention mechanism [J], Analytical Biochemistry, 2022, Vol. 651: 114695.

  33. Yi-Heng Zhu, Chengxin Zhang, Yan Liu, Gilbert S. Omenn, Peter L. Freddolino, Dong-Jun Yu*, and Yang Zhang*. Integrating Transcript Expression Profiles with Protein Homology Inferences for Gene Function Prediction [J], Genomics, Proteomics and Bioinformatics, 2022, 20 (5): 1013-1027.

  34. Lu-Xing Zhang, He Yan, Yan Liu, Jian Xu, Jiangning Song*, Dong-Jun Yu*. Enhancing characteristic gene selection and tumor classification by the robust Laplacian supervised discriminative sparse PCA [J], Journal of Chemical Information and Modeling, 2022, 62 (7): 1794-1807.

  35. He Yan, Liyong Fu, Yong Qi, Li Cheng, Qiaolin Ye and Dong-Jun Yu*. Learning a Robust Classifier for Short-Term Traffic State Prediction [J], Knowledge-based Systems, 2022, 242: 108368. 

  36. Yang Hua, Xiaoning Song*, Zhenhua Feng*, Xiao-Jun Wu, Josef Kittler, Dong-Jun Yu. CPInformer for Efficient and Robust Compound-Protein Interaction Prediction [J], IEEE Transactions on Computational Biology and Bioinformatics, 2022, In Press.

  37. 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, 2022, 23 (9): 14542-14556. 

  38. 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, 2022, 23 (1): bbab530.

  39. 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, 19: 6400-6416.

  40. 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 Intelligent Laboratory Systems, 2022, 220: 104458.

  41. 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, 19 (6): 3635-3645.

  42. 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.

  43. Ke Han#, Long-Chen Shen#, Yi-Heng Zhu, Jian Xu, Jiangning Song*, and Dong-Jun Yu*. MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network [J], Briefings in Bioinformatics, 2022, 23 (1): bbab445.

  44. Liang Rao, Ning-xin Jia, Jun Hu*, Dong-Jun Yu*, and Gui-jun Zhang*. ATPdock: a template-based method for ATP-specific protein-ligand docking [J], Bioinformatics, 2022, 38 (2):556-558.

  45. 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, 22 (6): bbab351.

  46. 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, 89 (12): 1911-1921. [TripletRes - the second best predictor in RR group in CASP 14]

  47. 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, 19 (5): 2749-2759.

  48. 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, 22 (6):bbab278.

  49. 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, 2022, 59 (9): 2051-2065.

  50. 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, 22 (6): bbab248.

  51. 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]

  52. 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.

  53. 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.

  54. 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.

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

  56. 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.

  57. 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

  58. 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.

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

  60. Xiaoning Song, Yao Chen, Zhen-Hua 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.

  61. 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.

  62. 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.

  63. 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.

  64. 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.

  65. 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

  66. Yanchao Li, Yong li Wang, Dong-Jun Yu, Ning Ye, 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.

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

  68. 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.

  69. 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]

  70. 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.

  71. 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.

  72. 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.

  73. 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.

  74. 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.

  75. 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: 993-1018.

  76. 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.

  77. 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.

  78. Jingzheng Li, Xibei Yang, Xiaoning Song, Jinghai Li, Pingxin Wang, and Dong-Jun Yu. Neighborhood attribute reduction: a multi-criterion approach [J]. International Journal of Machine Learning and Cybernetics, 2019, 10 (4): 731-742.

  79. 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.

  80. Jun Hu, Zi Liu, Dong-Jun Yu*, and Yang Zhang*. LS-align: an atom-level, flexible ligand structural alignment algorithm for high-throughput efficient virtual screening [J]. Bioinformatics, 2018, 34 (13): 2209-2218.

  81. 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.

  82. 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.

  83. 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.

  84. 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.

  85. 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.

  86. 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.

  87. 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.

  88. 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.

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

  90. 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

  91. 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

  92. 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.

  93. 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.

  94. 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.

  95. 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

  96. 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.

  97. 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.

  98. 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.

  99. 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.

  100. 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.

  101. 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.

  102. 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.

  103. 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

  104. 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.

  105. 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.

  106. 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.

  107. 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.

  108. 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.

  109. 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.

  110. 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.

  111. 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.

  112. 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)

  113. 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.

  114. 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.

  115. 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.

  116. 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.

  117. 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.

  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.

  119. 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.

  120. 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.

  121. 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.

  122. 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.

  123. 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.

  124. 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.

  125. 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.

  126. 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.

  127. 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 .

  128. 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).

  129. 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.

  130. 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.

  131. 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.

  132. 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.

  133. 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.

  134. 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.

  135. 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.

  136. 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.

  137. 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.

  138. 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.

  139. 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.

  140. 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.

  141. 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.

  142. 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.

  143. 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.

  144. 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.

  145. 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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