今天是:

师资队伍

教授

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陈兴

性别：男

出生日期：1984.11

职称、职务：教授

电话（手机）：

E-mail：8202305001@jiangnan.edu.cn

 

【学术简介】

江南大学三级教授、博导（教授博导均为31岁时直接破格），2019-23连续五年当选科睿唯安全球高被引科学家，2020-2023连续四年当选爱思唯尔中国高被引学者，连续多年入选斯坦福大学发布的全球前2%顶尖科学家“终身科学影响力”榜单和全球排名前十万科学家，江苏省333高层次人才培养工程培养对象，江苏省六大人才高峰高层次人才，教育部学科评估专家，科技创新2030—“新一代人工智能”重大项目会评专家，中国计算机学会杰出会员，江苏省生物信息学学会副理事长，中国工业与应用数学学会数学生命科学专业委员会秘书长，中国生物信息学学会（筹）多组学与整合生物学专业委员会常务委员，中国生物信息学学会（筹）表观遗传信息学专业委员会委员，中国工业与应用数学学会大数据与人工智能专业委员会委员，中国计算机学会生物信息学专业委员会委员，中国人工智能学会青年工作委员会委员，中国自动化学会智能健康与生物信息专业委员会委员，江苏省生物医学工程学会生物信息学专业委员会副主任，江苏省双创团队核心成员。中科院一区杂志Briefings in Bioinformatics（影响因子13.994）执行编辑，IEEE Journal of Biomedical and Health Informatics、Molecular Therapy-Nucleic Acids等四家SCI杂志副主编，International Journal of Biological Sciences、Computers in Biology and Medicine等十家SCI杂志编委，PLoS Computational Biology杂志特约副主编，International Journal of Molecular Sciences等七家SCI杂志首席特约编委，二十四家国际会议程序委员会成员。以一作或通讯发表SCI论文100余篇，以一作或通讯在Bioinformatics、PLoS Computational Biology、Briefings in Bioinformatics、Nucleic Acids Research四大生物信息主流期刊发表论文36篇，以一作或通讯发表影响因子大于10的论文28篇。论文被引共计15000余次，11篇论文入选最新一期ESI高被引论文，H-因子为60。获教育部高等学校科学研究优秀成果奖自然科学奖二等奖（排名第3）、江苏省科学技术奖三等奖（排名第1）、中国自动化学会自然科学奖二等奖（排名第1）、中国自动化学会自动化与人工智能创新团队奖（排名第2）、江苏省教育教学与研究成果奖一等奖（排名第1）、江苏省高等学校科学技术研究成果奖二等奖（排名第1）、多个国际会议的最佳论文奖、世界华人数学家大会新世界数学奖、德国红点概念设计奖等荣誉，主持国家自然科学基金重大研究计划培育项目、面上项目（2项）、青年基金、江苏省高层次人才项目等。

【工作及研究经历】：

2023.05-至今，江南大学云顶集团yd1233，教授、博导

2020.08-2023.04，中国矿业大学人工智能研究院，教授、博导（其中2020.8-2022.12为研究院唯一一个副院长）；

2016.05-2020.07，中国矿业大学信息与控制工程学院，教授、博导；

2012.07-2016.05，中国科学院数学与系统科学研究院，助理研究员；

2007.09-2012.06，中国科学院数学与系统科学研究院，运筹学与控制论博士学位；

2003.09-2007.06 山东大学，信息与计算科学专业学士学位。

【研究领域】

生物信息学，大数据分析预测，人工智能+应用。

【主要论著】（著作和论文）

主要论文：

[1] Xing Chen*, Li Huang. Computational model for drug research. Briefings in Bioinformatics. 2024 25(3): bbae158 (SCI, 影响因子9.5).

[2] Yan Zhao, Jun Yin, Li Zhang, Yong Zhang, Xing Chen*. Drug–drug interaction prediction: databases, web servers and computational models. Briefings in Bioinformatics. 2024 25(1): bbad445 (SCI, 影响因子9.5).

[3] Lihong Peng#, Wei Xiong#, Chendi Han, Zejun Li*, Xing Chen*. CellDialog: A Computational Framework for Ligand-receptor-mediated Cell-cell Communication Analysis. IEEE Journal of Biomedical and Health Informatics. 2024 28(1):580-591 (SCI, 影响因子7.7).

[4] Shu-Hao Wang, Yan Zhao, Chun-Chun Wang, Fei Chu, Lian-Ying Miao, Li Zhang, Linlin Zhuo*, Xing Chen*. RFEM: A framework for essential microRNA identification in mice based on rotation forest and multiple feature fusion. Computers in Biology and Medicine. 2024 171: 108177 (SCI, 影响因子7.7).

[5] Lihong Peng#, Pengfei Gao#, Wei Xiong, Zejun Li*, Xing Chen*. Identifying potential ligand–receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis. Computers in Biology and Medicine. 2024 171: 108110 (SCI, 影响因子7.7).

[6] Xing Chen#,*, Li Huang#. Computational model for disease research. Briefings in Bioinformatics. 2023 24(1): bbac615 (SCI, 影响因子13.994).

[7] Tian-Hao Li, Chun-Chun Wang, Li Zhang, Xing Chen*. SNRMPACDC: computational model focused on Siamese network and random matrix projection for anticancer synergistic drug combination prediction. Briefings in Bioinformatics. 2023 24(1): bbac503 (SCI, 影响因子13.994).

[8] Chen-Di Han, Chun-Chun Wang, Li Huang, Xing Chen*. MCFF-MTDDI: multi-channel feature fusion for multi-typed drug–drug interaction prediction. Briefings in Bioinformatics. 2023 24(4): bbad215 (SCI, 影响因子13.994).

[9] Li Zhang, Chun-Chun Wang, Yong Zhang, Xing Chen*. GPCNDTA: Prediction of drug-target binding affinity through cross-attention networks augmented with graph features and pharmacophores. Computers in Biology and Medicine. 2023 166:107512 (SCI, 影响因子7.7).

[10] Lihong Peng, Jingwei Tan, Wei Xiong, Li Zhang, Zhao Wang, Ruya Yuan, Zejun Li*, Xing Chen*. Deciphering ligand–receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data. Computers in Biology and Medicine. 2023 163:107137 (SCI, 影响因子7.7).

美国加州大学圣地亚哥分校Nathan E. Lewis副教授团队在Nature Reviews Genetics发表文章《The diversification of methods for studying cell–cell interactions and communication》引用了我们的工作，评价我们的方法侧重于在推断细胞相互作用之前，构建高置信度的受体一配体相互作用列表，这是细胞相互作用分析工作流的关键步骤（Some approaches also focus on building lists of high-confidence LRIs, a crucial step of the CCI analysis workflow, before inferring CCIs.）。

[11] Lihong Peng#, Ruya Yuan#, Chendi Han, Guosheng Han, Jingwei Tan, Zhao Wang, Min Chen*, Xing Chen*. CellEnBoost: A boosting-based ligand-receptor interaction identification model for cell-to-cell communication inference. IEEE Transactions on NanoBioscience. 2023 22(4): 705-715 (SCI, 影响因子3.9).

美国加州大学圣地亚哥分校Nathan E. Lewis副教授团队在Nature Reviews Genetics发表文章《The diversification of methods for studying cell–cell interactions and communication》引用了我们的工作，评价我们的方法侧重于在推断细胞相互作用之前，构建高置信度的受体一配体相互作用列表，这是细胞相互作用分析工作流的关键步骤（Some approaches also focus on building lists of high-confidence LRIs, a crucial step of the CCI analysis workflow, before inferring CCIs.）。

[12] Li Zhang, Chun-Chun Wang, Xing Chen*. Predicting drug–target binding affinity through molecule representation block based on multi-head attention and skip connection. Briefings in Bioinformatics. 2022 23(6): bbac468 (SCI, 影响因子13.994).

[13] Li Huang, Li Zhang, Xing Chen*. Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models. Briefings in Bioinformatics. 2022 23(5): bbac358 (SCI, 影响因子13.994, 被引18次).

[14] Li Huang, Li Zhang, Xing Chen*. Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models. Briefings in Bioinformatics. 2022 23(6): bbac407 (SCI, 影响因子13.994).

[15] Li Huang, Li Zhang, Xing Chen*. Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion. Briefings in Bioinformatics. 2022 23(6): bbac397 (SCI, 影响因子13.994, 被引13次).

[16] Chun-Chun Wang#, Chi-Chi Zhu#, Xing Chen*. Ensemble of kernel ridge regression-based small molecule–miRNA association prediction in human disease. Briefings in Bioinformatics. 2022 23(1): bbab431 (SCI, 影响因子13.994, 被引17次).

[17] Shu-Hao Wang, Chun-Chun Wang, Li Huang, Lian-Ying Miao, Xing Chen*. Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations. Briefings in Bioinformatics. 2022 23(1): bbab500 (SCI, 影响因子13.994, 被引10次).

[18] Chun-Chun Wang, Tian-Hao Li, Li Huang, Xing Chen*. Prediction of potential miRNA–disease associations based on stacked autoencoder. Briefings in Bioinformatics. 2022 23(2):bbac021 (SCI, 影响因子13.994, 被引22次).

[19] Xing Chen#,*, Li Huang#. Computational model for ncRNA research. Briefings in Bioinformatics. 2022 23(6): bbac472 (SCI, 影响因子13.994).

[20] Lihong Peng, Ruya Yuan, Chendi Han, Jingwei Tan, Zhao Wang, Min Chen*, Xing Chen*. Analyses of cell-to-cell communication combining a heterogeneous deep ensemble framework and scoring approaches from single-cell RNA sequencing data. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (EI, CCF B).

[21] Lihong Peng, Xianzhi He, Li Zhang, Xinhuai Peng, Yuankang Lu, Zejun Li*, Xing Chen*. A deep learning-based unsupervised learning method for spatially resolved transcriptomic data analysis. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (EI, CCF B).

[22] Xing Chen*, Lian-Gang Sun, Yan Zhao. NCMCMDA: miRNA–disease association prediction through neighborhood constraint matrix completion. Briefings in Bioinformatics. 2021 22(1):485-496 (SCI, 影响因子13.994, 被引126次, ESI高被引论文).

[23] Xing Chen*, Tian-Hao Li, Yan Zhao, Chun-Chun Wang, Chi-Chi Zhu. Deep-belief network for predicting potential miRNA-disease associations. Briefings in Bioinformatics. 2021 22(3): bbaa186 (SCI, 影响因子13.994, 被引77次, ESI高被引论文).

[24] Xing Chen*, Chi Zhou, Chun-Chun Wang, Yan Zhao. Predicting potential small molecule–miRNA associations based on bounded nuclear norm regularization. Briefings in Bioinformatics. 2021 22(6): bbab328 (SCI, 影响因子13.994, 被引29次).

[25] Yan Zhao, Chun-Chun Wang, Xing Chen*. Microbes and complex diseases: from experimental results to computational models. Briefings in Bioinformatics. 2021 22(3): bbaa158 (SCI, 影响因子13.994, 被引23次).

[26] Chun-Chun Wang, Yan Zhao, Xing Chen*. Drug-pathway association prediction: from experimental results to computational models. Briefings in Bioinformatics. 2021 22(3): bbaa061 (SCI, 影响因子13.994, 被引16次).

[27] Chun-Chun Wang, Chen-Di Han, Qi Zhao*, Xing Chen*. Circular RNAs and complex diseases: from experimental results to computational models. Briefings in Bioinformatics. 2021 22(6): bbab286 (SCI, 影响因子13.994, 被引96次, ESI高被引论文).

[28] Chi-Chi Zhu#, Chun-Chun Wang#, Yan Zhao, Ming-Cheng Zuo, Xing Chen*. Identification of miRNA–disease associations via multiple information integration with Bayesian ranking. Briefings in Bioinformatics. 2021 22(6): bbab302 (SCI, 影响因子13.994, 被引10次).

[29] Xing Chen*, Na-Na Guan, Ya-Zhou Sun, Jian-Qiang Li*, Jia Qu. MicroRNA-small molecule association identification: from experimental results to computational models. Briefings in Bioinformatics. 2020 21(1): 47-61 (SCI, 影响因子13.994, 被引116次, ESI高被引论文, the 20 most-cited articles from 2019 and 2020 in Briefings in Bioinformatics).

美国德克萨斯大学MD安德森癌症中心George A. Calin教授团队在Nature Reviews Drug Discovery发表文章《Noncoding RNA therapeutics —challenges and potential solutions》引用了我们的小分子药物-miRNA关联识别综述。

[30] Yan Zhao, Xing Chen*, Jun Yin, Jia Qu. SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association. RNA Biology. 2020 17(2): 281-291 (SCI, 影响因子4.766, 被引36次).

[31] Xing Chen*, Shao-Xin Li, Jun Yin, Chun-Chun Wang. Potential miRNA-disease association prediction based on kernelized Bayesian matrix factorization. Genomics. 2020 112(1): 809-819 (SCI, 影响因子4.310, 被引32次).

[32] Xing Chen*, Hongsheng Liu, Qi Zhao*. Editorial: Bioinformatics in Microbiota. Frontiers in Microbiology. 2020 11: 100 (SCI, 影响因子6.064).

[33] Xing Chen*, Chun-Chun Wang, Na-Na Guan. Computational Models in Non-Coding RNA and Human Disease. International Journal of Molecular Sciences. 2020 21(5):1557 (SCI, 影响因子6.208, 被引11次).

[34] Li-Hong Peng, Li-Qian Zhou*, Xing Chen*, Xue Piao*. A computational study of potential miRNA-disease association inference based on ensemble learning and kernel ridge regression. Frontiers in Bioengineering and Biotechnology. 2020 8:40 (SCI, 影响因子6.064, 被引31次).

[35] Xing Chen*, Di Xie, Qi Zhao, Zhu-Hong You. MicroRNAs and complex diseases: from experimental results to computational models. Briefings in Bioinformatics. 2019 20(2):515-539 (SCI, 影响因子13.994, 被引431次, ESI高被引论文, the 20 most-cited articles from 2019 and 2020 in Briefings in Bioinformatics).

[36] Xing Chen*, Ya-Zhou Sun, Hui Liu, Lin Zhang*, Jian-Qiang Li*, Jia Meng. RNA methylation and diseases: experimental results, databases, web servers and computational models. Briefings in Bioinformatics. 2019 20(3):896-917 (SCI, 影响因子13.994, 被引66次).

[37] Xing Chen*, Chi-Chi Zhu, Jun Yin. Ensemble of decision tree reveals potential miRNA-disease associations. PLoS Computational Biology. 2019 15(7): e1007209 (SCI, 影响因子4.779, 被引153次).

[38] Yan Zhao, Xing Chen*, Jun Yin. Adaptive boosting-based computational model for predicting potential miRNA-disease associations. Bioinformatics. 2019 35(22): 4730-4738 (SCI, 影响因子6.931, 被引110次).

[39] Lei Wang#, Zhuhong You#,*, Xing Chen*, Yang-Ming Li, Ya-Nan Dong, Li-Ping Li, Kai Zheng. LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities. PLoS Computational Biology. 2019 15(3):e1006865 (SCI, 影响因子4.779, 被引103次).

[40] Jia Qu, Xing Chen*, Ya-Zhou Sun, Yan Zhao, Shu-Bin Cai, Zhong Ming*, Zhu-Hong You*, Jian-Qiang Li. In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm. Molecular Therapy-Nucleic Acids. 2019 14:274-286 (SCI, 影响因子10.183, 被引49次).

[41] Na-Na Guan, Yan Zhao, Chun-Chun Wang, Jian-Qiang Li*, Xing Chen*, Xue Piao*. Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization. Molecular Therapy-Nucleic Acids 2019 17:164-174 (SCI, 影响因子10.183, 被引55次).

[42] Xing Chen*, Ya-Zhou Sun, Na-Na Guan, Jia Qu, Zhi-An Huang, Ze-Xuan Zhu, Jian-Qiang Li. Computational models for lncRNA function prediction and functional similarity calculation. Briefings in Functional Genomics. 2019 18(1):58-82 (SCI, 影响因子4.840, 被引124次).

[43] Chun-Chun Wang, Xing Chen*, Jun Yin, Jia Qu. An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy. RNA Biology. 2019 16(3):257-269 (SCI, 影响因子4.766, 被引28次).

[44] Jia Qu, Xing Chen*, Jun Yin, Yan Zhao, Zheng-Wei Li. Prediction of potential miRNA-disease associations using matrix decomposition and label propagation. Knowledge-Based Systems. 2019 186:104963 (SCI, 影响因子8.139, 被引22次).

[45] Li Zhang, Xing Chen*, Jun Yin. Prediction of Potential miRNA–Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder. Cells. 2019 8(9):1040 (SCI, 影响因子7.666, 被引38次).

[46] Jun Yin, Xing Chen*, Chun-Chun Wang, Yan Zhao, Ya-Zhou Sun. Prediction of small molecule-microRNA associations by sparse learning and heterogeneous graph inference. Molecular Pharmaceutics. 2019 16(7):3157-3166 (SCI, 影响因子5.364, 被引26次).

[47] Chun-Chun Wang, Xing Chen*, Jia Qu, Ya-Zhou Sun, Jian-Qiang Li. RFSMMA: A New Computational Model to Identify and Prioritize Potential Small Molecule-MiRNA Associations. Journal of Chemical Information and Modeling. 2019 59(4):1668-1679 (SCI, 影响因子6.162, 被引34次).

[48] Chun-Chun Wang, Xing Chen*. A Unified Framework for the Prediction of Small Molecule-MicroRNA Association Based on Cross-Layer Dependency Inference on Multilayered Networks. Journal of Chemical Information and Modeling. 2019 59(12):5281-5293 (SCI, 影响因子6.162, 被引18次).

[49] Ya-Wei Niu, Guang-Hui Wang*, Gui-Ying Yan, Xing Chen*. Integrating random walk and binary regression to identify novel miRNA-disease association. BMC Bioinformatics. 2019 20:59 (SCI, 影响因子3.307, 被引27次).

[50] Xing Chen*, Lei Wang, Jia Qu, Na-Na Guan, Jian-Qiang Li. Predicting miRNA-disease association based on inductive matrix completion. Bioinformatics. 2018 34(24): 4256-4265 (SCI, 影响因子6.931, 被引373次, ESI高被引论文).

[51] Xing Chen*, Di Xie, Lei Wang, Qi Zhao*, Zhu-Hong You, Hong-Sheng Liu. BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction. Bioinformatics. 2018 34(18): 3178-3186 (SCI, 影响因子6.931, 被引260次, ESI高被引论文).

[52] Xing Chen*, Jun Yin, Jia Qu, Li Huang. MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction. PLoS Computational Biology. 2018 14(8): e1006418 (SCI, 影响因子4.779, 被引268次, ESI高被引论文).

[53] Xing Chen#,*, Li Huang#, Di Xie, Qi Zhao. EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction. Cell Death & Disease. 2018 9: 3 (SCI, 影响因子9.685, 被引223次, The Best of Cell Death & Disease 2018-2019).

[54] Hui Liu#, Huaizhi Wang#, Zhen Wei#, Songyao Zhang, Gang Hua, Shao-Wu Zhang, Lin Zhang, Shou-Jiang Gao, Jia Meng*, Xing Chen*, Yufei Huang*. MeT-DB V2.0: elucidating context-specific functions of N6-methyl-adenosine methyltranscriptome. Nucleic Acids Research. 2018 46(Database issue): D281-D287 (SCI, 影响因子19.160, 被引100次)

法国巴斯德研究所Pascale Cossart教授团队在Nature Communications发表文章《Impact of the gut microbiota on the m6A epitranscriptome of mouse cecum and liver》引用了我们的m6A甲基化修饰数据库。

[55] Xing Chen#,*, Zhihan Zhou#, Jun Yin. Predicting microRNA-disease associations using bipartite local models and hubness-aware regression. RNA Biology. 2018 15(9): 1192-1205 (SCI, 影响因子4.766, 被引31次).

[56] Xing Chen#,*, Jun-Yan Cheng#, Yan Zhao. ELLPMDA: Ensemble learning and link prediction for miRNA-disease association prediction. RNA Biology. 2018 15(6): 807-818 (SCI, 影响因子4.766, 被引61次).

[57] Xing Chen*, Chun-Chun Wang, Jun Yin, Zhu-Hong You*. Novel Human miRNA-Disease Association Inference Based on Random Forest. Molecular Therapy-Nucleic Acids. 2018 13:568-579 (SCI, 影响因子10.183, 被引85次).

[58] Jia Qu, Xing Chen* Ya-Zhou Sun, Jian-Qiang Li, Zhong Ming. Inferring potential small molecule-miRNA association based on triple layer heterogeneous network. Journal of Cheminformatics. 2018 10:30 (SCI, 影响因子8.489, 被引59次).

[59] Hui Liu, Yan Zhao, Lin Zhang, Xing Chen*. Anti-cancer drug response prediction using neighbor-based collaborative filtering with global effect removal. Molecular Therapy-Nucleic Acids. 2018 13:303-311 (SCI, 影响因子10.183, 被引59次).

[60] Xing Chen*, De-Hong Zhang, Zhu-Hong You*. A heterogeneous label propagation approach to explore the potential associations between miRNA and disease. Journal of Translational Medicine. 2018 16:348 (SCI, 影响因子8.440, 被引43次) .

[61] Xing Chen*, Na-Na Guan, Jian-Qiang Li*, Gui-Ying Yan. GIMDA: Graphlet Interaction-based MiRNA-Disease Association prediction. Journal of Cellular and Molecular Medicine. 2018 22(3):1548-1561 (SCI, 影响因子5.295, 被引28次) .

[62] Xing Chen#,*, Yao Gong#, De-Hong Zhang, Zhu-Hong You, Zheng-Wei Li. DRMDA: deep representations-based miRNA–disease association prediction. Journal of Cellular and Molecular Medicine. 2018 22(1): 472-485 (SCI, 影响因子5.295, 被引68次) .

[63] Xing Chen#,*, Le-Yi Wang#, Li Huang. NDAMDA: Network distance analysis for MiRNA-disease association prediction. Journal of Cellular and Molecular Medicine. 2018 22(5): 2884-2895 (SCI, 影响因子5.295, 被引30次) .

[64] Lin Zhang, Xing Chen*, Na-Na Guan, Hui Liu and Jian-Qiang Li. A hybrid interpolation weighted collaborative filtering method for anti-cancer drug response prediction. Frontiers in Pharmacology. 2018 9:1017 (SCI, 影响因子5.988, 被引39次).

[65] Na-Na Guan, Ya-Zhou Sun, Zhong Ming, Jian-Qiang Li*, Xing Chen*. Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction. Frontiers in Pharmacology. 2018 9:1152 (SCI, 影响因子5.988, 被引26次).

[66] Dongqing Guo, Colin E. Murdoch, Tianhua Liu, Jia Qu, Shihong Jiao, Yong Wang*, Wei Wang*, Xing Chen*. Therapeutic Angiogenesis of Chinese Herbal Medicines in Ischemic Heart Disease: A Review. Frontiers in Pharmacology. 2018 9:428 (SCI, 影响因子5.988, 被引41次).

[67] Ya-Zhou Sun, De-Hong Zhang, Shu-Bin Cai, Zhong Ming*, Jian-Qiang Li*, Xing Chen*. MDAD: a special resource for microbe-drug associations. Frontiers in Cellular and Infection Microbiology. 2018 8:424 (SCI, 影响因子6.073, 被引46次).

[68] Xing Chen*, Jing-Ru Yang, Na-Na Guan, Jian-Qiang Li. GRMDA: Graph Regression for MiRNA-Disease Association Prediction. Frontiers in Physiology. 2018 9: 92 (SCI, 影响因子4.755, 被引23次).

[69] Yan Zhao, Xing Chen*, Jun Yin. A novel computational method for the identification of potential miRNA-disease association based on symmetric non-negative matrix factorization and Kronecker regularized least square. Frontiers in Genetics. 2018 9:324 (SCI, 影响因子4.772, 被引32次).

[70] Xing Chen*, Jia Qu, Jun Yin. TLHNMDA: Triple Layer Heterogeneous Network based inference for MiRNA-Disease Association prediction. Frontiers in Genetics. 2018 9:234 (SCI, 影响因子4.772, 被引26次).

[71] Lei Wang#, Zhu-Hong You#, *, Xing Chen*, Xin Yan, Gang Liu, Wei Zhang. RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions using Drug Structure and Protein Sequence Information. Current Protein & Peptide Science. 2018 19(5):445-454 (SCI, 影响因子3.118, 被引102次).

[72] Lei Wang#, Zhu-Hong You#, *, Xing Chen*, Shi-Xiong Xia, Feng Liu, Xin Yan, Yong Zhou, Ke-Jian Song. A Computational-Based Method for Predicting Drug–Target Interactions by Using Stacked Autoencoder Deep Neural Network. Journal of Computational Biology. 2018 25(3): 361-373 (SCI, 影响因子1.549, 被引143次, the fifth most-read paper from the journal).

[73] Xing Chen#,*, Chenggang Clarence Yan#, Xu Zhang#, Zhu-Hong You*. Long non-coding RNAs and complex diseases: from experimental results to computational models. Briefings in Bioinformatics. 2017 18(4):558-576 (SCI, 影响因子13.994, 被引513次, ESI高被引论文).

美国匹兹堡大学Da Yang教授团队在Science Advances发表文章《LincRNA-immunity landscape analysis identifies EPIC1 as a regulator of tumor immune evasion and immunotherapy resistance》引用了我们的长链非编码RNA-疾病关联预测综述。

[74] Zhuhong You#, Zhi-An Huang#, Ze-Xuan Zhu*, Gui-Ying Yan, Zheng-Wei Li, Zhenkun Wen, Xing Chen*. PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction. PLoS Computational Biology. 2017 13(3): e1005455 (SCI, 影响因子4.779, 被引340次, ESI高被引论文).

[75] Xing Chen#,*, Yu-An Huang#, Zhu-Hong You*, Gui-Ying Yan, Xue-Song Wang*. A Novel Approach based on KATZ measure to Predict Associations of Human Microbiota with Non-Infectious Diseases. Bioinformatics. 2017 33(5):733-739 (SCI, 影响因子6.931, 被引193次)

[76] Xing Chen#,*, Li Huang#. LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction. PLoS Computational Biology. 2017 13(12): e1005912 (SCI, 影响因子4.779, 被引212次).

[77] Xing Chen#,*, Qiaofeng Wu#, Gui-Ying Yan. RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction. RNA Biology. 2017 14(7): 952-962 (SCI, 影响因子4.766, 被引129次).

[78] Jian-Qiang Li, Zhu-Hong You*, Xiao Li, Ming Zhong, Xing Chen*. PSPEL: In Silico Prediction of Self-interacting Proteins from Amino Acids Sequences using Ensemble Learning. IEEE-ACM Transactions on Computational Biology and Bioinformatics. 2017 14(5):1165-1172 (SCI, 影响因子3.702, 被引54次).

[79] Xing Chen*, Ya-Wei Niu, Guang-Hui Wang*, Gui-Ying Yan. MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA–disease association prediction. Journal of Translational Medicine. 2017 15:251 (SCI, 影响因子8.440, 被引43次).

[80] Xing Chen*, Ya-Wei Niu, Guang-Hui Wang*, Gui-Ying Yan. HAMDA: Hybrid Approach for MiRNA-Disease Association prediction. Journal of Biomedical Informatics. 2017 76(2017): 50-58 (SCI, 影响因子8.000, 被引48次).

[81] Zhi-An Huang#, Xing Chen#,*, Ze-Xuan Zhu*, Hongsheng Liu, Gui-Ying Yan, Zhu-Hong You, Zhenkun Wen. PBHMDA: Path-Based Human Microbe-Disease Association prediction. Frontiers in Microbiology. 2017 8:233 (SCI, 影响因子6.064, 被引73次) .

[82] Yu-An Huang, Zhu‑Hong You*, Xing Chen*, Zhi-An Huang, Shanwen Zhang, Gui-Ying Yan. Prediction of microbe–disease association from the integration of neighbor and graph with collaborative recommendation model. Journal of Translational Medicine. 2017 15:209 (SCI, 影响因子8.440, 被引74次).

[83] Xing Chen*, Ya-Zhou Sun, De-Hong Zhang, Jian-Qiang Li*, Gui-Ying Yan, Ji-Yong An, Zhu-Hong You. NRDTD: a database for clinically or experimentally supported non-coding RNAs and drug targets associations. Database. 2017 2017: bax057 (SCI, 影响因子4.462, 被引52次).

美国德克萨斯大学MD安德森癌症中心George A. Calin教授团队在Nature Reviews Drug Discovery发表文章《Noncoding RNA therapeutics —challenges and potential solutions》引用了我们的工作，评价我们提供了一个相当全面的关于小分子对miRNA表达影响的数据库（offer a reasonably comprehensive repository of data regarding the influence of small molecules on miRNA expression）。

[84] Ya-Zhou Sun, De-Hong Zhang, Zhong Ming, Jian-Qiang Li*, Xing Chen*. DLREFD: a database providing associations of long non-coding RNAs, environmental factors and phenotypes. Database. 2017 2017:bax084 (SCI, 影响因子4.462, 被引13次).

[85] Fan-Rong Meng, Zhu-Hong You*, Xing Chen*, Yong Zhou, Ji-Yong An. Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures. Molecules. 2017 22(7): 1119 (SCI, 影响因子4.927, 被引61次).

[86] Jian-Qiang Li#, Zhi-Hao Rong#, Xing Chen*, Gui-Ying Yan. MCMDA: Matrix Completion for MiRNA-Disease Association prediction. Oncotarget. 2017 8(13):21187-21199 (SCI, 被引176次).

[87] Xing Chen#,* Chenggang Clarence Yan#, Xiaotian Zhang, Xu Zhang, Feng Dai, Jian Yin, Yongdong Zhang. Drug–target interaction prediction: databases, web servers and computational models. Briefings in Bioinformatics. 2016 17(4):696-712 (SCI, 影响因子13.994, 被引483次, ESI高被引论文)

美国加州大学旧金山分校Katherine S. Pollard教授团队在Nature Reviews Genetics发表文章《Navigating the pitfalls of applying machine learning in genomics》引用了我们的药物靶点相互作用预测综述。

苏黎世联邦理工大学Gisbert Schneider教授团队在Nature Chemistry发表文章《Counting on natural products for drug design》引用了我们的药物靶点相互作用预测综述。

美国威尔康奈尔医学院Olivier Elemento教授团队在Nature Communications发表文章《A Bayesian machine learning approach for drug target identification using diverse data types》引用了我们的药物靶点相互作用预测综述。

清华大学Jianyang Zeng教授团队在Cell Systems发表文章《MONN: A Multi-objective Neural Network for Predicting Compound-Protein Interactions and Affinities》引用了我们的药物靶点相互作用预测综述。

[88] Xing Chen#, Biao Ren#, Ming Chen, Quanxin Wang, lixin Zhang*, Guiying Yan*. NLLSS: Predicting Synergistic Drug Combinations based on semi-supervised learning. PLoS Computational Biology. 2016 12(7): e1004975 (SCI, 影响因子4.779, 被引236次)

[89] Xing Chen#,*, Chenggang Clarence Yan#, Xu Zhang, Zhu-Hong You, Lixi Deng, Ying Liu*, Yongdong Zhang, Qionghai Dai. WBSMDA: Within and Between Score for MiRNA-Disease Association prediction. Scientific Reports. 2016 6:21106 (SCI, 影响因子4.996, 被引332次).

[90] Xing Chen#,*, Chenggang Clarence Yan#, Xu Zhang, Zhu-Hong You, Yu-An Huang, Gui-Ying Yan*. HGIMDA: Heterogeneous Graph Inference for MiRNA-Disease Association prediction. Oncotarget. 2016 7(40): 65257-65269 (SCI, 被引203次).

[91] Yu-An Huang#, Zhu-Hong You#,*, Xing Chen*, Keith Chan, Xin Luo. Sequence-based Prediction of Protein-Protein Interactions Using Weighted Sparse Representation Model Combined with Global Encoding. BMC Bioinformatics. 2016 17:184 (SCI, 影响因子3.307, 被引137次).

[92] Zheng-Wei Li#, Zhu-Hong You#, *, Xing Chen*, Jie Gui, Ru Nie. Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics. International Journal of Molecular Sciences. 2016 17(9): 1396 (SCI, 影响因子6.208, 被引34次).

[93] Xing Chen*, Zhu-Hong You*, Gui-Ying Yan, Dun-Wei Gong. IRWRLDA: Improved Random Walk with Restart for LncRNA-Disease Association prediction. Oncotarget. 2016 7(36):57919-57931 (SCI, 被引180次).

[94] Xing Chen#, *, Yu-An Huang#, Xue-Song Wang, Zhu-Hong You*, Keith Chan. FMLNCSIM: Fuzzy Measure-based lncRNA functional similarity calculation model. Oncotarget. 2016 7(29):45948-45958 (SCI, 被引100次).

[95] Yu-An Huang#, Xing Chen#, *, Zhu-Hong You*, De-Shuang Huang, Keith Chan. ILNCSIM: improved lncRNA functional similarity calculation model. Oncotarget. 2016 7(18):25902-25914 (SCI, 被引124次).

[96] Xing Chen*. Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA. Scientific Reports. 2015 5:13186 (SCI, 影响因子4.996, 被引217次)

[97] Xing Chen#,*, Chenggang Clarence Yan#, Xiaotian Zhang, Zhaohui Li, Lixi Deng, Yongdong Zhang, Qionghai Dai. RBMMMDA: predicting multiple types of disease-microRNA associations. Scientific Reports. 2015 5: 13877 (SCI, 影响因子4.996, 被引172次).

[98] Xing Chen#,*, Chenggang Clarence Yan#, Cai Luo, Wen Ji, Yongdong Zhang, Qionghai Dai. Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity. Scientific Reports. 2015 5:11338 (SCI, 影响因子4.996, 被引195次)

[99] Xing Chen*. KATZLDA: KATZ measure for the lncRNA-disease association prediction. Scientific Reports. 2015 5:16840 (SCI, 影响因子4.996, 被引199次)

[100] Xing Chen* and Gui-ying Yan*. Semi-supervised learning for potential human microRNA-disease associations inference. Scientific Reports. 2014 4:5501 (SCI, 影响因子4.996, 被引343次)

[101] Xing Chen* and Gui-ying Yan*. Novel human lncRNA-disease association inference based on lncRNA expression profiles. Bioinformatics. 2013 29(20): 2617-2624 (SCI, 影响因子6.931, 被引499次).

[102] Xing Chen, Ming-xi Liu and Gui-ying Yan*. Drug-target interaction prediction by random walk on the heterogeneous network. Molecular BioSystems. 2012 8(7): 1970-1978 (SCI, 影响因子4.212, 被引500次. Top ten most accessed articles in June 2012; Featured in the top 10% of the most highly cited articles published in the latest Impact Factor window (2011-2012)).

以色列特拉维夫大学Roded Sharan教授团队在Nature Reviews Genetics发表文章《Network propagation: a universal amplifier of genetic associations》引用了我们的工作，评价我们的方法成功地应用了网络传播方法预测药物靶点之间的关系（Chen et al. aimed to predict drug–target relationships based on the assumption that similar drugs target similar proteins. To this end, they successfully applied network propagation to predict drug–target relationships by using an integrated network that included target–target, drug–target and drug–drug relationships.）。

清华大学Jianyang Zeng教授团队在Nature Communications发表文章《A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information》引用了我们的药物靶点预测工作。

[103] Xing Chen, Ming-xi Liu and Gui-ying Yan*. RWRMDA: predicting novel human microRNA–disease associations. Molecular BioSystems. 2012 8(10): 2792-2798 (SCI, 影响因子4.212, 被引379次).

【科研、教学项目】

科研项目：

  1. 国家自然科学基金重大研究计划培育项目，92370131，基于人工智能方法的蛋白质-配体相互作用预测研究，2024/01-2026/12，84万（含间接经费），在研，项目主持；

  2. 国家自然科学基金面上项目，61972399，基于多源数据融合的药物响应和药物相关非编码RNA靶点预测研究，2020/01-2023/12，72万（含间接经费），在研，项目主持；

  3. 国家自然科学基金面上项目，61772531，多视角识别长非编码RNA和人类复杂疾病关联预测研究，2018/01-2018/12，19.2万（含间接经费），结题，项目主持；

  4. 国家自然科学基金青年基金，11301517，多视角识别人类复杂疾病相关microRNA的数学模型与方法研究，2014/01-2016/12，22万，结题，项目主持；

  5. 江苏省第十四批“六大人才高峰”高层次人才项目，SWYY-089，长非编码RNA生物大数据处理和预测，2017/07-2020/06，4万，结题，项目主持；

  6. 国家自然科学基金重点项目，11931008，基于图与组合优化的生物数据和网络数据挖掘算法研究，2020/01-2024/12，270万，在研，项目骨干（排名第二，第二单位负责人）；

  7. 国家自然科学基金重点项目，11631014，图理论和算法研究及其在生物信息学中的应用，2017/01-2021/12，230万，结题，项目骨干；

  8. 江苏省“双创计划”团队项目，新型非晶合金的制备原理及其在能源环境领域的开发应用，2018/10-2021/10，500万，结题，项目核心成员；

  9. 国家自然科学基金重大研究计划培育项目，91229127，非可控性炎症调控网络体内定量监视预测技术研究，2013/01-2015/12，110万，结题，项目骨干；

  10. 国家自然科学基金专项基金项目数学天元基金，11326042，协同药物研发中的关键数学问题，2013/10-2014/09，20万，结题，项目骨干；

  11. 广东省移动互联网应用中间件工程技术研究中心开放课题，基于机器学习和复杂网络方法的药物靶点相互作用预测研究，2018/12-2019/11，5万，结题，项目主持；

  12. 广东省移动互联网应用中间件工程技术研究中心开放课题，疾病相关miRNA预测研究，2017/12-2018/11，5万，结题，项目主持；

  13. 深圳大学深圳移动互联网应用中间件技术工程实验室开放课题，基于智能算法的长非编码RNA与疾病关联类型预测研究，2019/06-2020/06，5万，结题，项目主持；

  14. 深圳大学大数据系统计算技术国家工程实验室开放课题，SZU-BDSC-XY-2018-01，基于智能算法的lncRNA功能预测研究，2018/04-2020/03，5万，结题，项目主持；

  15. 深圳大学深圳移动互联网应用中间件技术工程实验室开放课题，基于生物大数据的人类复杂疾病相关微生物预测研究，2020/12-2022/12，2.9万，结题，项目主持；

  16. 中国矿业大学重大项目培育专项，深度学习方法在大数据分析预测上的应用研究，2020/08-2023/07，50万，在研，项目主持；

  17. 中国矿业大学重大项目培育专项，基于生物医学信息的非编码RNA药物靶点数据分析与智能预测方法研究，2019/08-2022/07，30万，结题，项目主持；

  18. 中国矿业大学优秀学者专项，人工智能方法在大数据分析预测上的应用研究，2020/08-2020/12，20万，结题，项目主持；

  19. 中国矿业大学学科前沿科学研究专项面上项目，基于计算智能算法的药物靶点预测研究，2017/01-2019/12，20万，结题，项目主持；

  20. 中国科学院国家数学与交叉科学中心数学与生物医学交叉研究部重大专项，重大慢性多发疾病的动态网络系统构建，2012/07-2016/05，参加。

教学项目：

中国矿业大学2018年在线课程建设项目，大数据时代的人工智能算法及其应用，2018/07-2020/07，2万，结题，项目主持

【科研、教学成果及获奖】

科研获奖：

1. 教育部高等学校科学研究优秀成果奖自然科学奖二等奖《复杂生物数据的特征建模及高效学习理论与应用》（排名第3），2016；

2. 中国自动化学会自然科学奖二等奖《基于智能算法的复杂疾病相关microRNA生物标志物预测》（排名第1），2020；

3. 江苏省科学技术奖三等奖《基于大数据处理与挖掘技术的生物信息学研究》（排名第1），2019；

4. 江苏省教育教学与研究成果奖高校自然科学研究类一等奖《基于复杂网络和机器学习的生物信息学研究》（排名第1），2018；

5. 江苏省高等学校科学技术研究成果奖二等奖《基于人工智能方法的microRNA关联预测研究》（排名第1），2021；

6. 第七届图论与组合算法国际研讨会青年论文奖，2017；

7. 第五届国际网络博弈论大会最佳论文奖, 2014；

8. 德国红点概念设计奖，2021；

9. 第十届太湖奖设计大赛产品组铜奖，2021；

10. 中国设计智造大奖产业组佳作奖，2021；

11. 2016—2017年度徐州市自然科学优秀学术论文二等奖，2018；

12. 2018-2019年度徐州市自然科学优秀学术论文二等奖，2020；

13. 2020-2021年度徐州市自然科学优秀学术论文三等奖，2022；

14. 沈阳市自然科学学术成果奖二等奖，2018。

教学获奖：

中国矿业大学2018年度高质量通识教育公选课（大数据时代的人工智能算法及其应用）, 2018

【荣誉与奖励】

  1. 2023年中国高被引学者，2024；

  2. 2023年科睿唯安全球高被引科学家，2023；

  3. 2022年科睿唯安全球高被引科学家，2022；

3. 2021年科睿唯安全球高被引科学家，2021；

4. 2020年科睿唯安全球高被引科学家，2020；

5. 2019年科睿唯安全球高被引科学家，2019；

6. 2022年中国高被引学者，2023；

7. 2021年中国高被引学者，2022；

8. 2020年中国高被引学者，2021；

9. 斯坦福大学发布的全球前2%顶尖科学家（入围“终身科学影响力”榜单），连续多年；

10. 全球排名前十万科学家，连续多年；

11. 中国自动化学会自动化与人工智能创新团队奖（排名第2），2021；

12. 江苏省第六期“333高层次人才培养工程”培养对象，2022；

13. 江苏省第十四批“六大人才高峰”高层次人才，2017；

14. 第四届世界华人数学家大会新世界数学奖（第一次评选），2007；

15. 第六届淮海科学技术奖—科技英才奖，2018；

16. 第六届徐州市优秀科技工作者, 2018。

【在读硕、博士人数】

博士4人、硕士6人

【已毕业硕、博士人数】

博士4人、硕士18 人

【以上资料更新日期】

2024年6月