姓　　名：吴梦云
职　　称：副教授
研究方向：高维数据，变量选择，网络模型，生物统计
教授课程：实变函数、概率论、随机过程
E - mail：wu.mengyun@mail.shufe.edu.cn；电话：**

研究项目
序号
项目名称
项目编号
项目来源
起止时间
项目经费

1
基于多类型数据融合的可重复网络生物标志物检测
**
国家自然科学青年基金项目
2015.1-2017.12
26万

2
基于网络结构的多层次变量选择方法及应用
2018LD02
全国统计科学研究重大项目
2018.10-2020.09
15万

3
多源高维数据变量间相互作用的整合分析及应用
18CG42
上海市晨光计划
2019.01-2021.12
2万

4
多源高维数据的变量选择与整合分析及其在生物医疗领域的应用
19PJ**
上海市浦江人才
2019.10-2021.09
30万

5
面向多层次异质性癌症数据的网络分析方法及理论
**
国家自然科学基金面上项目
2021.01-2024.12
51万


研究领域
高维数据，变量选择，网络模型，生物统计

教育经历
2008.9-2013.6
中山大学，概率论与数理统计，博士
2004.9-2008.6
中山大学，统计学，学士
工作经历
2018.7-至今 上海财经大学，统计与管理学院，副教授
2016.8-2018.7 耶鲁大学，生物统计系，博士后
2013.7-2018.6 上海财经大学，统计与管理学院，讲师
2011.3-2011.8 香港城市大学，电子工程系，研究助理

研究成果
1. Li Y, Wang F, Wu M^*, Ma S (2020). Integrative functional linear model for genome-wide association studies with multiple traits. Biostatistics. doi:10.1093/biostatistics/kxaa043
2. Wu M, Yi H, Ma S^*(2020). Vertical integration methods for gene expression data analysis. Briefings in Bioinformatics. doi: 10.1093/bib/bbaa169
3. Teran Hidalgo SJ, Wu M^*, Ma S^*(2020). NCutYX: a package for clustering analysis of multilayer omics data. Bioinformatics, 36:1976-1977.
4. Wu M, Zhang Q, Ma S* (2020). Structured gene-environment interaction analysis. Biometrics, 76:23-35.
5. Wu M, Ma S* (2019). Robust semiparametric gene-environment interaction analysis using sparse boosting. Statistics in Medicine, 38:4625-4641.
6. Xu Y#, Wu M#, Zhang Q, Ma S* (2019). Robust identification of gene-environment interactions for prognosis using a quantile partial correlation approach. Genomics, 111:1115-1123.
7. Wang S, Wu M^*, Ma S^* (2019). Integrative analysis of cancer omics data for prognosis modeling.Genes,10(8), 604.
8. Li Y, Li R, Qin Y, Wu M^*, Ma S^* (2019). Integrative interaction analysis using threshold gradient directed regularization.Applied Stochastic Models in Business and Industry, 35(2), 354-375.
9. Wu M, Ma S^* (2019). Robust genetic interaction analysis. Briefings in Bioinformatics, 20(2): 624-637
10. Xu Y, Zhong T, Wu M^*, Ma S^*(2019). Histopathological imaging–environment interactions in cancer modeling. Cancers, 2019, 11(4): 579.
11. Zhong T, Wu M^*, Ma S^*(2019). Examination of independent prognostic power of gene expressions and histopathological imaging features in cancer.Cancers,11(3), 361.
12. Teran Hidalgo SJ, Zhu T, Wu M^*, Ma S^*(2018).Overlapping clustering of gene expression data using penalized weighted normalized cut.Genetic Epidemiology, 42: 796-811.
13. Li Y, Bie R, Hidalgo SJH, Qin Y, Wu M^*, Ma S^*(2018). Assisted gene expression-based clustering with AWNCut. Statistics in Medicine, 37: 4386-4403.
14. Xu Y, Wu M^*, Ma S, Ejaz Ahmed S (2018). Robust gene-environment interaction analysis using penalized trimmed regression.Journal of Statistical Computation and Simulation, 88: 3502-3528.
15. Li T^*, Wu M, Zhou Y (2018). A unified semi-empirical likelihood ratio confidence interval for treatment effects in the two sample problem with length-biased data. Statistics and Its Interface. 11: 531-540.
16. Wu M, Zhu L, Feng X^*(2018). Network-based feature screening with applications to genome data. The Annals of Applied Statistics, 12: 1250-1270.
17. Wu M, Huang J, Ma S^*(2018). Identifying gene-gene interactions using penalized tensor regression. Statistics in Medicine, 37(4): 598-610.
18. Wu M，Zang Y, Zhang S，Huang J, Ma S^*(2017). Accommodating missingness in environmental measurements in gene-environment interaction analysis. Genetic Epidemiology, 41: 523-554.
19. Teran Hidalgo SJ, Wu M, Ma S^*(2017). Assisted clustering of gene expression data using ANCut. BMC Genomics, 18: 623.
20. Ou-Yang L, Zhang X, Dai D^*, Wu M, Zhu Y, Liu Z, Yan H (2016).Protein complex detection based on partially shared multi-view clustering.BMC Bioinformatics, 17: 371.
21. Zhang X, Ou-Yang L, Dai D^*, Wu M, Zhu Y, Yan H (2016).Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks.BMC Bioinformatics, 17: 358.
22. Wu M, Zhang X, Dai D^*, Ou-Yang L, Zhu Y, Yan H (2016). Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer. BMC Bioinformatics, 17:108.
23. Zhang X, Ou-Yang L, Zhu Y, Wu M, Dai D^*(2015). Determining minimum set of driver nodes in protein-protein interaction networks. BMC Bioinformatics,16(1):146.
24. Wu M, Dai D^*, Zhang X, Zhu Y (2013). Cancer subtype discovery and biomarker identification via a new robust network clustering algorithm. PLoS ONE, 8(6): e66256.
25. Zhu Y, Zhang X, Dai D^*, Wu M (2013). 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(1): 219-225.
26. Wu M, Dai D^*, Yan H (2012). PRL-Dock: Protein-ligand docking based on hydrogen bond matching and probabilistic relaxation labeling. Proteins: Structure, Function, and Bioinformatics, 80(9): 2137-2153.
27. Wu M, Dai D^*, Shi Y, Yan H, Zhang X (2012). Biomarker identification and cancer classification based on microarray data using Laplace naive Bayes model with mean shrinkage. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(6): 1649-1662.
28. Zhang X, Dai D^*, Ou-Yang L, Wu M (2012). Exploring overlapping functional units with various structure in protein interaction networks. PLoS ONE, 7(8): e43092.



奖励，荣誉
2015年上海财经大学青年教师教学竞赛三等奖(理工组二等奖)
社会工作
Elected Member of the International Statistical Institute
Referee for BMC Genomics, BMC Bioinformatics, Statistics & Probability Letters, Statistics and Its Interface, Annals of the Institute of Statistical Mathematics, etc.

学术报告（2008年以来）
Integrative clustering of multidimensional omics data. The 2018 ICSA Applied Statistics Symposium. New Jersey, USA, June, 14-17, 2018.
Robust gene-environment interaction analysis using penalized trimmed regression. International Workshop on Perspectives On High-dimensional Data Analysis (HDDA-VIII-2018). Marrakesh, Morocco, April, 09-13, 2018.
Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer. The 4th IBS-China International Biostatistical Conference. Shanghai, China, July, 02-03, 2016.
Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer. 2016 ICSA China Statistics Conference. Qingdao, China, June, 24-25, 2016.