所在地:湖北武汉市 入职年份: 资料待完善 学历: 资料待完善 毕业院校: 资料待完善
从事领域 网络
擅长能力 机器学习,统计模式识别,复杂网络分析,生物信息学。
My research interests include machine learning, statistical pattern recognition, complex network analysis, bioinformatics.I am currently looking for talented and motivated students. If you are interested, please send me your CV. 我的研究方向:机器学习,统计模式识别,复杂网络分析,生物信息学。 目前正在招收研究生,请对我的研究方向感兴趣的同学与我联系。说明:我的研究方向主要侧重建模和算法,主要解决生物数据分析和预测处理等方面的问题。欢迎有自动化,计算机,数学,生物信息等教育背景的同学报考!注意:请编程零基础的同学慎重考虑获奖及指导学生获奖情况:2016年度中国地质大学(武汉)地大学者(2017-2019)2016年度最受欢迎课程“信号与系统”2015年度自动化学院青年教师讲课比赛第二名2018年度中国地质大学青年教师竞赛二等奖(第三名)2019年度中国地质大学青年教师竞赛二等奖2020年度中国地质大学首届教师创新大赛二等奖指导硕士研究生获2019年度中国地质大学科技报告会一等奖:获奖人:张德鑫(本年度自动化学院仅1名硕士研究生获得该奖项) 指导学生获2016年度亚太地区数学建模竞赛一等奖;获奖人:吴崇,张少杰,王一煜指导学生获2017年度美国数学建模竞赛二等奖(2组)获奖人:吴崇,张少杰,王一;曹加旺、卢春晓、王美琦主持项目:图小波及稀疏概率图模型在重构高精度蛋白质网络中的应用(国家自然科学基金青年基金 No.11401110 2015-2017 经费22万)基于稀疏概率图模型及多源数据融合的动态蛋白质相互作用网络建模与应用研究(湖北省自然科学基金一般面上项目NO. 2016CFB481 2017-2018 经费3万)基于高维多源融合数据的蛋白质网络重构研究(中国地质大学(武汉)C类学术创新基地复杂系统先进控制与智能地学仪器研究中心开放基金项目 No.AU2015CJ008 2015-2017 经费6万)面向高维融合数据的稀疏图模型及在蛋白质相互作用网络重构方面的应用(广东省高校自然科学研究项目No.2013KJCX0086 2013-2015 经费6万)发表论文:Yuan Zhu, Dexin Zhang, Xiaofei Zhang, Ming Yi#, Le Ou-Yang, Mengyun Wu, "EC-PGMGR: ensemble clustering based on probability graphical model with graph regularization for single-cell RNA-seq data", Frontiers in Genetics, 11(572242), 2020,DOI:doi: 10.3389/fgene.2020.572242.Houwang Zhang, Yuan Zhu*, Hanying Zheng, NAMF: a nonlocal adaptive mean filter for removal of salt-and-pepper noise, Mathematical Problems in Engineering, 2021(4127679): 1-10, 2021.Yuan Zhu#, Jiufeng Zhou, Hong Yan, "A unified formulation of a class of graph matching technique", Pattern Recognition, 95: 223-234, 2019.X. T. Huang, Yuan Zhu*#, "Inference of cellular level signaling networks using single-cell gene expression data in C.elegans reveals mechanisms of cell fate specification", Bioinformatics, 33(10): 1528-1535, 2017.Chong Wu, Tao Wu, Kaiyuan Fu, Yuan Zhu, Yongbo Li, Wangyong He and Shengwen Tang, “AMOBH: adaptive multiobjective black hole algorithm”, Computational Intelligence and Neuroscience, 2017(6153951): 1-19, 2017.X. T. Huang, Yuan Zhu*#, H. Yan, “Integrative C. elegans protein-protein interaction network with reliability assessment based on probabilistic graph model”, Molecular Biosystems, 12(1): 85-92, 2016.Yuan Zhu#, X. F. Zhang D. Q. Dai and M. Y. Wu, “Identifying spurious interactions and predicting missing interactions in the protein-protein interaction networks via a generative network model”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10: 219-225, 2013.Yuan Zhu#, W. Zhou, D. Q. Dai and H. Yan, “Identification of DNA-binding and protein-binding proteins using enhanced graph wavelet features”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10: 1017-1031, 2013.Yuan Zhu#, W. Gao and D. Li, “Characterization of generators for multiresolution analyses with composite dilations”, Abstract and Applied Analysis, 2011(DOI:10.1155/2011/850850).L. Ou-Yang, X. F. Zhang, D. D. Dai, M. Y. Wu, Yuan Zhu, Z. Liu and H. Yan, “Protein complex detection based on partially shared multi-view clustering”, BMC Bioinformatics, 17: 371, 2016.X. F. Zhang, L. Ou-Yang, D. D. Dai, M. Y. Wu, Yuan Zhu and Hong Yan, “Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks”, BMC Bioinformatics, 17: 358, 2016.M. Y. Wu, X. F. Zhang, D. D. Dai, L. Ou-Yang, Yuan Zhu, and Hong Yan, “Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer”, BMC Bioinformatics, 17: 108, 2016.J. Zhou, Yuan Zhu* and Hong Yan, “Local topology preserved tensor models for graph matching”, IEEE International Conference on Systems Man and Cybernetics 2015: 2153-2157, 2015.X. F. Zhang, L.Ou-yang, Yuan Zhu, M. Y. Wu and D.Q.Dai, “Determining minimum set of driver nodes in protein-protein interaction networks”, BMC Bioinformatics, vol. 16, p.146, 2015.M. Y. Wu, D. Q. Dai, X. F. Zhang and Yuan Zhu, “Cancer subtype discovery and biomarker identification via a new robust network clustering algorithm”, PLoS ONE, vol. 8, p. e66256, 2013.其他研究成果:朱媛,张晓飞,欧阳乐,吴梦云。生物分子网络中的信息挖掘方法,电子工业出版社,2020。培养学生:彭晓宇,硕士研究生(工程硕士),2016-2018,武汉船舶职业技术学校专任教师;刘 松,硕士研究生(工学硕士),2017-2020,武汉中原电子集团有限公司;张德鑫,硕士研究生(工学硕士),2018-2021,乐元素科技(北京)股份有限公司张厚望,硕士研究生(工学硕士),2018-2021,进入香港城市大学攻读博士学位陈 磊,硕士研究生(工程硕士),2018-2021,深信服科技股份有限公司