欧阳乐 Dr. Le Ou-Yang 

理学博士、深圳大学电子与信息工程学院副教授、硕士生导师、IEEE Member。

联系方式(Contact):
Email: leouyang@szu.edu.cn

Tel：0755－26659561

地址：深圳市南山区白石路深圳大学电子与信息工程学院N802

简介(Biography)：

欧阳乐，博士，副教授，硕士研究生导师，博士生（留学）导师。于2015年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。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、深圳大学荔园优青获得者。目前已在IEEE TCYB、Bioinformatics、Briefings in Bioinformatics、Pattern Recognition、BMC Bioinformatics、BMC Genomics、IEEE/ACM TCBB等国际期刊发表SCI论文40余篇，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，AAAI、IJCAI、IEEE BIBM等国际学术会议程序委员会委员。

研究兴趣(Research Interests)：

• 机器学习、数据挖掘、生物信息学；

• Machine Learning, Data Mining, Bioinformatics.

研究生招生 (Recruitment)：

硕士生、留学博士生、博士后：信息与通信工程。

主讲课程(Teaching Courses)：

机器学习、复变函数与场论。

获奖与荣誉 (Honors)：

• 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

• 2017年获深圳市南山区“领航人才”C类

• 深圳市海外高层次人才（孔雀计划）C类

• 广东省珠江人才计划青年拔尖人才

• 深圳大学“荔园优青”

主要学术兼职 (Academic Service)

• IEEE HPCC 2018 PC member

• GIW 2018 PC member

• 中国计算机学会生物信息学专业委员会委员

• 中国人工智能学会生物信息学与人工生命专业委员会委员

• 中国自动化学会智能健康与生物信息专业委员会委员

• Frontiers in Genetics编委、客座编辑

• IEEE BIBM，IJCAI，AAAI程序委员会委员

科研项目 (Research Projects)：

• 国家自然科学基金青年基金项目“基于异构泛癌症组学数据的生物标志物识别研究” （1-2019.12），61602309。

• 深圳市科技计划基础研究项目“基于概率图模型的医学大数据挖掘与算法研究”（3-2020.3）

•  国家自然科学基金面上项目，2022.01-2025.12

• 国家自然科学基金青年基金项目，2017.01-2019.12

• 广东省自然科学基金面上项目，2019.10-2022.09

• 深圳市科技计划基础研究项目，2018.03-2020.03

• 深圳市高端人才科研启动项目，2019.01-2021.12

学术成果(Publications)

部分期刊论文 Some Selected Journal Papers

• Le Ou-Yang, Dehan Cai, Xiao-Fei Zhang, Hong Yan, WDNE: an integrative graphical model for inferring differential networks from multi-platform gene expression data with missing values, Briefings in Bioinformatics, 2021, 10.1093/bib/bbab086.

• Fan Lu, Yilong Lin, Chongbin Yuan, Xiao-Fei Zhang, Le Ou-Yang*, EnTSSR: a weighted ensemble learning method to impute single-cell RNA sequencing data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021, 10.1109/TCBB.2021.3110850.

• Le Ou-Yang, Xiao-Fei Zhang, Hong Yan, Sparse regularized low-rank tensor regression with applications in genomic data analysis. Pattern Recognition, 107: 107516 (2020)

• Xiao-Fei Zhang, Le Ou-Yang*, Ting Yan, Xiaohua Hu, Hong Yan, A joint graphical model for inferring gene networks across multiple subpopulations and data types，IEEE Transactions on Cybernetics, 2021, 51(2): 1043-1055.

• Zi-Chao Zhang, Xiao-Fei Zhang, Min Wu, Le Ou-Yang*, 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.

• Le Ou-Yang, Xiao-Fei Zhang*, Xiaohua Hu, Hong Yan, Differential network analysis via weighted fused conditional Gaussian graphical model, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020, 17(6): 2162 - 2169

• Xiao-Fei Zhang, Le Ou-Yang*, 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.

• Xiao-Fei Zhang, Le Ou-Yang*, Shuo Yang, Xiaohua Hu, Hong Yan, DiffNetFDR: Differential network analysis with false discovery rate control, Bioinformatics, 2019,35(17): 3184-3186.

• 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.

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

• 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.

• 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.

• X. F. Zhang,L.Ou-Yang*, 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.

• X. F. Zhang ,L. Ou-Yang* and H. Yan, Incorporating prior information into differential network analysis using non-paranormal graphical models, Bioinformatics, 33.16 (2017): 2436–2445.

• 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.

• 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.

• 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.

• 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,

• Le Ou-Yang, Xiao-Fei Zhang, Min Wu, Xiao-Li Li, “Node-based learning of differential networks from multi-platform gene expression data”, Methods, vol. 129, pp. 41-49, 2017.

• Xiao-Fei Zhang, Le Ou-Yang(通讯作者), Shuo Yang, Xiaohua Hu, Hong Yan, “DiffGraph: An R package for identifying gene network rewiring using differential graphical models”, Bioinformatics, vol. 34, no. 9, pp. 1571-1573, 2017.

• 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.

• Xiao-Fei Zhang, Le Ou-Yang(通讯作者), Hong Yan, “Incorporating prior information into differential network analysis using nonparanormal graphical models”, Bioinformatics, vol. 33, no. 16, pp. 2436-2445, 2017.

• Le Ou-Yang, Hong Yan, Xiao-Fei Zhang, “A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks”, BMC Bioinformatics, vol. 18, no. 13, p 463, 2017.

• Le Ou-Yang, Hong Yan, Xiao-Fei Zhang, “Identifying differential networks based on multi-platform gene expression data”, Molecular BioSystems, vol. 13, no. 1, pp. 183-192, 2017.

• Xiao-Fei Zhang, Le Ou-Yang(通讯作者), Xing-Ming Zhao, Hong Yan, “Differential network analysis from cross-platform gene expression data”, Scientific Reports, vol. 6, p 34112, 2016.

• Le Ou-Yang, Xiao-Fei Zhang, Dao-Qing Dai, Meng-Yun Wu, Yuan Zhu, Zhiyong Liu, Hong Yan, “Protein complex detection based on partially shared multi-view clustering”, BMC Bioinformatics, vol. 17, no. 1, p 371, 2016.

• Le Ou-Yang, Min Wu, Xiao-Fei Zhang, Dao-Qing Dai, Xiao-Li Li, Hong Yan, “A Two-Layer Integration Framework for Protein Complex detection”, BMC Bioinformatics, vol. 17, no. 1, p 100, 2016.

• Le Ou-Yang, Dao-Qing Dai, Xiao-Fei Zhang, “Detecting protein complexes from signed protein-protein interaction networks”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 12, no. 6, pp. 1333-1344, 2015.

• Le Ou-Yang, Dao-Qing Dai, Xiao-Li Li, Min Wu, Xiao-Fei Zhang, Peng Yang, “Detecting temporal protein complexes from dynamic protein-protein interaction networks”, BMC Bioinformatics, vol. 15, no. 1, p 335, 2014.

• Le Ou-Yang, Dao-Qing Dai, Xiao-Fei Zhang, “Protein complex detection via weighted ensemble clustering based on Bayesian nonnegative matrix factorization”, PLoS ONE, vol. 8, no. 5, p e62158, 2013.

部分会议论文 Some Selected Conference Papers

• LeOu-Yang, Hong Yan and Xiao-Fei Zhang, Identifying protein complexes via multi-network clustering. Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on. IEEE, 2016.

专利 Patents

• 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