欧阳乐,博士,助理教授,特聘副研究员,硕士研究生导师,IEEE会员。于2015年6月获中山大学概率论与数理统计专业统计模式识别方向理学博士学位;2013年-2014年,赴新加坡南洋理工大学计算机科学系交流访问。2015年-2016年在香港城市大学电子工程系从事博士后研究。现任深圳大学助理教授,特聘副研究员。主要从事数据挖掘、机器学习和生物信息学等领域的科研和教学工作。广东省珠江人才计划青年拔尖人才,深圳市海外高层次人才(孔雀计划)C类,南山区“领航人才”C类获得者。目前已在IEEE TCYB、Bioinformatics、Pattern Recognition、BMC Bioinformatics、BMC Genomics、IEEE/ACM TCBB、Scientific Reports、Methods和Molecular BioSystems等国际期刊发表SCI论文40余篇,在IEEE BIBM、APBC发表会议论文2篇。获授权发明专利1项。担任Bioinformatics、PLoS Computational Biology、Briefings in Bioinformatics、IEEE JBHI、Communication Biology、Methods、IEEE/ACM TCBB等重要刊物审稿人。


研究兴趣:机器学习、数据挖掘和医学大数据


研究内容:基于机器学习方法的生物医学大数据挖掘,多源异构数据融合分析,聚类分析、图模型、矩阵分解模型


教授课程:复变函数、机器学习


研究生招生方向:

学术型硕士:信息与通信工程

专业型硕士:电子与通信工程


学术服务:

IEEE会员

中国计算机学会生物信息学专业委员会委员

GIW 2018、IEEE HPCC 2018、IEEE BIBM 2018、2019、IJCAI 2020程序委员会委员


近五年主持科研项目清单:

1.国家自然科学基金青年基金项目,2017.01-2019.12

2.广东省自然科学基金面上项目,2019.10-2022.09

3.深圳市科技计划基础研究项目,2018.03-2020.03

4.深圳大学新引进教师科研启动项目,2017.05-2019.05

5.深圳市高端人才科研启动项目,2019.01-2021.12


期刊论文:

[1] Zi-Chao Zhang, Xiao-Fei Zhang, Min Wu, Le Ou-Yang(通讯), Xing-Ming Zhao, Xiao-Li Li, A Graph Regularized Generalized Matrix Factorization Model for Predicting Links in Biomedical Bipartite Networks, Bioinformatics, 2020, 36(11): 3474-3481.

[2] Xiao-Fei Zhang, Le Ou-Yang(通讯), Shuo Yang, Xing-Ming Zhao, Xiaohua Hu, Hong Yan, EnImpute: imputing dropout events in single cell RNA sequencing data via ensemble learning, Bioinformatics, 2019, 35(22): 4827-4829.

[3] Xiao-Fei Zhang, Le Ou-Yang(通讯), Ting Yan, Xiaohua Tony Hu, Hong Yan, A Joint Graphical Model for Inferring Gene Networks Across Multiple Subpopulations and Data Types, IEEE Transactions on Cybernetics, 2019, accepted.

[4]Xiao-Fei Zhang, Le Ou-Yang(通讯), Shuo Yang, Xiaohua Hu, Hong Yan, DiffNetFDR: Differential network analysis with false discovery rate control, Bioinformatics, 2019, 35(17): 3184-3186. (2017 IF: 5.481, Rank: 3/59, Q1)

[5]Le Ou-Yang, Xiao-Fei Zhang, Xing-Ming Zhao, Debby D Wang, Fu Lee Wang, Baiying Lei, Hong Yan, Joint Learning of Multiple Differential Networks With Latent Variables, IEEE Transactions on Cybernetics, 2019, 49(9): 3494-3506.(2018 IF: 10.387, Rank: 1/61, Q1)

[6]Jiang Huang, Min Wu, Fan Lu, Le Ou-Yang(通讯), Zexuan Zhu, Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization, BMC Bioinformatics, 2019,20(19): 657.

[7]Jia-Juan Tu, Le Ou-Yang, Xiaohua Hu, Xiao-Fei Zhang, Identifying gene network rewiring by combining gene expression and gene mutation data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, accepted.(2017 IF: 2.428, Rank: 12/123, Q1)

[8]Ting Xu, Le Ou-Yang, Xiaohua Hu, Xiaofei Zhang, Identifying gene network rewiring by integrating gene expression and gene network data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, accepted.(2017 IF: 2.428, Rank: 12/123, Q1)

[9]X. F. Zhang, L.Ou-Yang(通讯), S. Yang, X. Hu and H. Yan, DiffGraph: An R package for identifying gene network rewiring using differential graphical models, Bioinformatics, 34.9 (2018): 1571-1573.(2017 IF: 5.481, Rank: 3/59, Q1)

[10]L.Ou-Yang, H. Yan and X. F. Zhang*, A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks, BMC Bioinformatics, 18.13 (2017):463.(2017 IF:2.213, Rank:Q1)

[11]L.Ou-Yang, X. F. Zhang*, M. Wu and X. L. Li, Node-based Learning of Differential Networks from Multi-platform Gene Expression Data, Methods, 129 (2017): 41-49.(2017 IF:3.998,Rank:Q1)

[12]X. F. Zhang, L. Ou-Yang*(通讯) and H. Yan, Incorporating prior information into differential network analysis using non-paranormal graphical models, Bioinformatics, 33.16 (2017): 2436–2445.(2017 IF: 5.481, Rank: 3/59, Q1)

[13]X.F.Zhang, L.Ou-Yang*(通讯), X. M. Zhao and H. Yan, Differential network analysis from cross-platform gene expression data. Scientific Reports, 6, 2016.(Rank: Q1) 

[14]L.Ou-Yang, X.F.Zhang, D.Q.Dai, M.Y.Wu, Y.Zhu, Z.Liu and H.Yan, Protein complex detection based on partially shared multi-view clustering. BMC Bioinformatics, vol. 17: 371, September 2016.(Rank: Q1)

[15]X.F.Zhang, L.Ou-Yang, D.Q.Dai, M.Y.Wu, Y.Zhu and H.Yan, Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks. BMC Bioinformatics, vol. 17: 358, September 2016.

[16]L.Ou-Yang, M.Wu, X.F.Zhang, D.Q.Dai, X.L.Li, and H.Yan, A Two-Layer Integration Framework for Protein Complex detection, BMC Bioinformatics, vol. 17:100, February 2016. 

[17]M.Y.Wu, X.F.Zhang, D.Q.Dai, L.Ou-Yang, Y.Zhu and H.Yan, Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer, BMC Bioinformatics,vol. 17:108, February 2016.

[18]L.Ou-Yang, D.Q.Dai and X.F.Zhang, Detecting protein complexes from signed protein-protein interaction networks, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.12:6, 2015. 

[19]X.F.Zhang, L.Ou-Yang, X.Hu and D.Q.Dai, Identifying binary protein-protein interactions from AP-MS data, BMC Genomics, 16.1 (2015): 745.

[20]X.F.Zhang, L.Ou-Yang, Y.Zhu, M.Y.Wu and D.Q.Dai, Determining minimum set of driver nodes in protein-protein interaction networks, BMC Bioinformatics, vol. 16:146, May 2015.

[21]L. Ou-Yang, D. Q. Dai, X. L. Li, M. Wu, X. F. Zhang, and P. Yang, Detecting temporal protein complexes from dynamic protein-protein interaction networks, BMC Bioinformatics, vol. 15:335, October 2014. 

[22]X.F.Zhang, D.Q.Dai, L.Ou-Yang and H.Yan, Detecting overlapping protein complexes based on a generative model with functional and topological properties, BMC Bioinformatics, vol. 15:186, June 2014.

[23]P.Yang, X.Su, L.Ou-Yang, HN.Chua, XL.Li, K.Ning. Microbial community pattern detection in human body habitats via ensemble clustering framework. BMC Systems Biology 2014, 8(Suppl 4):S7.

[24]L.Ou-Yang, D.Q.Dai and X.F.Zhang, Protein complex detection via weighted ensemble clustering based on Bayesian nonnegative matrix factorization, PLoS ONE, vol. 8, issue 5, e62158, May 2013. 

[25]M.Wu, L.Ou-Yang and X.L.Li, Protein Complex Detection via Effective Integration of Base Clustering Solutions and Co-complex Affinity Scores, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 14.3 (2017): 733-739.

[26]L.Ou-Yang, H.Yan and X.F.Zhang, Identifying differential networks based on multi-platform gene expression data, Molecular BioSystems, 13.1 (2017): 183-192.

[27]X. F. Zhang, L.Ou-Yang*(通讯) and H. Yan, Node-based differential network analysis in genomics, Computational Biology and Chemistry, 69 (2017): 194-201.

[28]Rui Guo, Yan-Ran Li, Shan He, Le Ou-Yang, Yiwen Sun, Zexuan Zhu, “RepLong: de novo repeat identification using long read sequencing data”, Bioinformatics, vol. 34, no. 7, pp. 1099-1107, 2017.


专利:

欧阳乐,戴道清,张晓飞,蛋白质复合体挖掘的加权组装聚类方法,国家发明专利,专利号:CN 201310104854X


获奖信息:

2011年获中山大学优秀研究生

2013年获博士研究生国家奖学金

2017年获深圳市海外高层次人才(孔雀计划)C类

2018年获南山区“领航人才”C类

2018年获广东省珠江人才计划青年拔尖人才


指导学生获奖情况:

指导学生获得2018年度美国大学生数学建模竞赛一等奖,获奖人:林明雄,吴永,蓝锋博

指导学生获得2018年度美国大学生数学建模竞赛二等奖,获奖人:张子超,郭玉盈,黎永财

指导学生获得2019年度美国大学生数学建模竞赛一等奖,获奖人:黄华振,林浩钦,黄少聪

指导学生获得2019年度美国大学生数学建模竞赛二等奖,获奖人:梁浩,王璟,邱钰茹

指导学生获得2020年度美国大学生数学建模竞赛一等奖,获奖人:陈禧龙,麦晶滢,罗亦丰

指导学生获得2020年度美国大学生数学建模竞赛二等奖,获奖人:黄森,王荧荧,林嘉伟

指导学生获得2018年中国大学生计算机设计大赛二等奖,获奖人:吴永,张子超,林明雄

指导学生获得2019年中国大学生计算机设计大赛二等奖2项(王璟,赵麟),三等奖1项(陈泽伟)

指导学生获得2019年mathorcup高校数学建模挑战赛二等奖1项(赵麟)



2017年国家级大学生创新创业训练计划1项(张子超,林明雄,吴永,区耀中)

2018年国家级大学生创新创业训练计划1项(蔡德涵,谢杰恩,梁浩)

2019年省级大学生创新创业训练计划1项(林嘉伟,黄森,王荧荧,陈嘉祺,彭丽波)

2020年省级大学生创新创业训练计划2项(袁崇斌、周润东)


研究生招生:

招收有志于从事科学研究的学生(学术型和专业型均可)

要求:

a) 高等数学、线性代数、数值分析、概率论与数理统计等课程基础扎实;

b) 至少精通两门编程语言:Matlab、Python、C++、Java;

c) 英语的听说读写能力强,有较强的英文阅读和写作能力,一般要求英语六级500分以上;

d) 对研究方向感兴趣,对科学研究有热情,不怕吃苦、不怕失败、做事认真负责。混学位者请勿联系。

从事科学研究并不指毕业后只是在高校和研究所工作,也指愿意毕业后去著名公司从事研发工作或研究院工作、或出国留学继续攻读博士学位等。


本科生招生:

招收有志于在深圳大学攻读硕士研究生或有志于在本科/硕士阶段后攻读国外大学硕士/博士学位的1-3年级本科生。

要求:有志于在未来从事数据挖掘、机器学习方向学术研究的学生,尤其侧重于生物医学数据分析和机器学习算法研究。要求数学和编程相关课程学业成绩较高。能够把课余的50~70%的时间全部用于科研中,只有集中精力做好一件事才能做好。

修读或自修以下课程:线性代数、概率统计、Matlab/Python、多元统计分析、矩阵论。


办公地点:N802

办公电话:0755-26659561

E-mail:leouyang(AT) szu.edu.cn