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综合介绍 General Introduction 于会，男，博士，副教授，硕士生导师，人工智能协会会员，粒计算与知识发现专业委员会委员，TNNLS、TCYB、TNSE、IEEE JBHI、TCSS、TCBB、Neural Networks、Computers in Biology and Medicine、Expert Syst. Appl、Clinical Pharmacokinetics、BIB、Artificial Intelligence Review、JMLC等国际期刊审稿人。主要研究方向为智能决策、粒计算与三支决策、复杂网络、数据挖掘、生物信息等。目前主要从事智能信息处理、复杂网络、三支决策和粒计算、药物间相互作用等方向的研究。先后主持航空科学支撑基金项目2项、陕西省自然基金项目2项，西安市科技计划攻关项目1项、校基础研究基金项目等，作为核心成员参与完成国家自然基金、国家863计划项目、国家部委预研基金项目、国家部委基金课题等多个项目的研究。获国防科学技术三等奖2项和陕西省科学技术三等奖2项，申请发明专利2项、软件著作权5项，发表论文40余篇，SCI和EI收录20余篇。 其中一篇论文入选科技部中国科学技术信息研究所发布的2015年“ 中国百篇最具影响国内学术论文”。  个人相册

教育教学

教育教学 Education and teaching 招生信息 学生信息 招生专业：　　计算机科学与技术，计算机应用技术，计算机技术，软件工程。研究方向：　　数据挖掘、生物信息、复杂网络、三支决策及粒计算、欢迎有志于机器学习、数据挖掘等领域的学生保送或报考。请将个人简历，成绩单及相关资料发送到邮箱：huiyu@nwpu.edu.cn。 学生姓名 学号 学历 专业 曹夕 2015201764

荣誉获奖

获奖信息 The winning information 曾获国防科学技术三等奖2项，陕西省科学技术三等奖奖3项。一篇论文入选科技部中国科学技术信息研究所发布的2015-2017年“ 中国百篇最具影响国内学术论文”及2017年度“领跑者5000-中国精品科技期刊顶尖学术论文”。

科学研究

团队信息 Team Information

学术成果

社会兼职 Social Appointments

综合介绍

学术成果 Academic Achievements [25]Hui Yu, Jing Wang, Chao Song, Jian-Yu Shi*. Identifying the reaction centers of molecule based on dual-view representation. Knowledge-Based Systems, 2024, 292:111606 DOI:10.1016/j.knosys.2024.111606 (SCI, IF 8.8).[24]Hui Yu, Jing Wang, Shi-Yu Zhao, Omayo Silver, Zun Liu, JingTao Yao, Jian-Yu Shi*, GGI-DDI: Identification for key molecular substructures by granule learning to interpret predicted drug–drug interactions, Expert Systems with Applications, Volume 240, 2024, 122500. DOI:10.1016/j.eswa.2023.122500. (SCI, IF 8.5).[23]Bing-Xue Du, Yi Xu, Siu-Ming Yiu, Hui Yu*, Jian-Yu Shi*, ADMET property prediction via multi-task graph learning under adaptive auxiliary task selection, iScience, Volume 26, Issue 11, 2023, 108285. DOI:10.1016/j.isci.2023.108285. (SCI, IF 5.8).[22]Yu, H*., Wang, Q.F. & Shi, J.Y*. Data Augmentation Generated by Generative Adversarial Network for Small Sample Datasets Clustering. Neural Processing Letters (2023). DOI:10.1007/s11063-023-11315-z(SCI, IF 3.1). [21]Hui Yu*, KangKang Li, WenMin Dong, ShuangHong Song, Chen Gao and JianYu Shi*, Attention-based cross domain graph neural network for prediction of drug–drug interactions, Briefings in Bioinformatics, 2023, bbad155. DOI:10.1093/bib/bbad155(SCI, IF 11.622)[20]H. Yu*, K. Li and J. Shi*, "DGANDDI: Double Generative Adversarial Networks for Drug-Drug Interaction Prediction," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022.  DOI:10.1109/TCBB.2022.3219883(SCI, IF 3.702. CCF B)[19]Bing-Xue Du, Peng-Cheng Zhao, Bei Zhu, Siu-Ming Yiu, Arnold K Nyamabo, Hui Yu*, Jian-Yu Shi*, MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction, Bioinformatics, Volume 38, Issue Supplement_1, July 2022, Pages i325–i332. DOI:10.1093/bioinformatics/btac222 (SCI, IF 6.931)[18]Hui Yu*, ShiYu Zhao, JianYu Shi*, STNN-DDI: a Substructure-aware Tensor Neural Network to predict Drug–Drug Interactions, Briefings in Bioinformatics, Volume 23, Issue 4, July 2022, bbac209. DOI:10.1093/bib/bbac209 (SCI, IF 11.622) [17]Feng, Yi-Yang, Hui Yu*, Yue-Hua Feng and Jian-Yu Shi*. “Directed graph attention networks for predicting asymmetric drug-drug interactions.” Briefings in bioinformatics, Volume 23, Issue 3, May 2022, bbac151. DOI:10.1093/bib/bbac151(SCI, IF 11.622)[16]Du, Bing-Xue, Yuan Qin, Yanyan Jiang, Yi Xu, Siu-Ming Yiu, Hui Yu* and Jian-Yu Shi*. “Compound-protein interaction prediction by deep learning: databases, descriptors and models.” Drug discovery today, Volume 27, Issue 5, May 2022, Pages 1350-1366. DOI:10.1016/j.drudis.2022.02.023 (SCI, IF 7.851)[15]Bei Zhu, Yi Xu, Pengcheng Zhao, Siu Ming Yiu, Hui Yu*, Jian-Yu Shi*. NNAN: Nearest Neighbor Attention Network to predict drug-microbe associations. Frontiers in Microbiology, 626. 2022, Published: 11April 2022. DOI:10.3389/fmicb.2022.846915(SCI, IF 5.640)[14]Hui Yu*, WenMin Dong, JianYu Shi*, RANEDDI: Relation-Aware Network Embedding for Drug-Drug Interaction Prediction. Information Sciences, vol. 582, 2022, pp. 167–180. DOI:10.1016/j.ins.2021.09.008(SCI, IF 6.795)[13]Arnold K Nyamabo, Hui Yu*, Zun Liu, Jian-Yu Shi*, Drug–drug interaction prediction with learnable size-adaptive molecular substructures, Briefings in Bioinformatics, 2021, 23(1), bbab441. DOI:10.1093/bib/bbab441(SCI, IF 11.622)[12]Arnold K. Nyamabo, Hui Yu* and Jian-Yu Shi*, SSI–DDI: substructure–substructure interactions for drug–drug interaction prediction, Briefings in Bioinformatics, 2021, 22(6), bbab133. DOI:10.1093/bib/bbab133, (SCI, IF 11.622)[11]H. Yu*, L.Y. Chen, J.T. Yao, A three-way density peak clustering method based on evidence theory, Knowledge-Based Systems, 211:106532, 2021. DOI:10.1016/j.knosys.2020.106532 (SCI, IF 8.038)[10]H. Yu*, L.Y. Chen, J.T. Yao and X.N. Wang, A three-way clustering method based on an improved DBSCAN algorithm, Physica A: Statistical Mechanics and its Applications, 535:122289, 2019. DOI:10.1016/j.physa.2019.122289(SCI, IF 2.924)[9]Chen L, Yu H*. Emergency Alternative Selection Based on an E-IFWA Approach. IEEE Access, 2019, 7: 44431-44440. (ESI, SCI, IF 4.098)[8]Shi, Jian-Yu*, Kui-Tao Mao, Hui Yu and Siu-Ming Yiu. “Detecting drug communities and predicting comprehensive drug–drug interactions via balance regularized semi-nonnegative matrix factorization.” Journal of cheminformatics, 2019, 11(1): 28. (SCI, IF 5.326)[7]Hui Yu*, Kui-Tao Mao, Jian-Yu Shi*, Hua Huang, Zhi Chen, Kai Dong, Siu-Ming Yiu. Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization, BMC systems biology, 2018, 12(1): 14. (SCI, IF 2.505)[6]Yu Hui*, Cao Xi, Liu Zun, Li Yongjun. Identifying key nodes based on improved structural holes in complex networks. Physica A: Statistical Mechanics and its Applications, 2017, 486: 318~327. DOI:10.1016/j.physa.2017.05.028(SCI, IF 2.076)[5]Yong-Jun L*, Chao Y, Hui Y, et al. Link prediction in microblog retweet network based on maximum entropy model. Acta Physica Sinica, 2016, 65(2). (SCI, IF 2.924)[4]Shi J Y*, Liu Z, Yu H, et al. Predicting drug-target interactions via within-score and between-score. BioMed research international, 2015, 2015. (SCI)[3]Hui Y.*, Luyuan C., Xi C., Zun L., Yongjun L. Identifying Top-K Important Nodes Based on Probabilistic-Jumping Random Walk in Complex Networks. 6th Int. Conference on Complex Networks & Their Applications, Lyon, France. 2017.11.29-12.01[2]于会*, 刘尊, 李勇军. 基于多属性决策的复杂网络节点重要性综合评价方法. 物理学报. 2013, 62(2):020204 http://doi: 10.7498/aps.62.020204(SCI)[1]Hui Y.*, Zun L., Yongjun L. Using local improved Structural Holes method to identify key nodes in complex networks, 5th International Conference on Measuring Technology and Mechatronics Automation, HongKong, China. 2013.01.16-01.17 (EI)