高维数据，变量选择，网络模型，生物统计

1.  Li Y, Wu M, Wu M*, Ma S (2023+). Identification of influencing factors on self-reported count data with multiple potential inflated values. The Annals of Applied Statistics, accepted.

2.  Li Y, Wu M, Ma S, Wu M* (2023+). ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data. Genome Biology, accepted.

3.   Wu M, Wang F, Ge Y, Ma S, Li Y* (2023+). Bi-level structured functional analysis for genome-wide association studies. Biometrics, accepted.

4.  Qin X, Ma S, Wu M* (2022+). Two-level Bayesian interaction analysis for survival data incorporating pathway information. Biometrics, accepted.

5.   Zhong T, Zhang Q, Huang J, Wu M^*, Ma S^* (2023). Heterogeneity analysis via integrating multi-sources high-dimensional data with applications to cancer studies. Statistica Sinica, 33, 729-758.

6.    Cheng C, Feng X, Li X, Wu M* (2022). Robust analysis of cancer heterogeneity for high-dimensional data. Statistics in Medicine, 41, 5448-5462.

7.    Xu Y, Wu M^*, Ma S^* (2022). Multidimensional molecular measurements-environment interaction analysis for disease outcomes. Biometrics, 78, 1524-1554.

8.   Li Y, Xu S, Ma S, Wu M*(2022). Network-based cancer heterogeneity analysis incorporating multi-view of prior information. Bioinformatics, 38, 2855-2862.

9.   Li Y, Wang F, Wu M^*, Ma S (2022). Integrative functional linear model for genome-wide association studies with multiple traits. Biostatistics, 23, 574-590.

10. Qin X, Ma S, Wu M^* (2021). Gene-gene interaction analysis incorporating network information via a structured Bayesian approach. Statistics in Medicine, 40, 6619-6633.

11.  Wu M, Qin X, Ma S^* (2021). GEInter: an R package for robust gene-environment interaction analysis. Bioinformatics, 39, 3691-3692.

12.   Wu M, Yi H, Ma S^*(2021). Vertical integration methods for gene expression data analysis. Briefings in Bioinformatics, 22:1-14.

13.   Teran Hidalgo SJ, Wu M^*, Ma S^*(2020). NCutYX: a package for clustering analysis of multilayer omics data. Bioinformatics, 36:1976-1977.

14.   Wu M, Zhang Q, Ma S* (2020). Structured gene-environment interaction analysis. Biometrics, 76:23-35.

15.   Wu M, Ma S* (2019). Robust semiparametric gene-environment interaction analysis using sparse boosting. Statistics in Medicine, 38:4625-4641.

16.   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.

17.   Wang S, Wu M^*, Ma S^* (2019). Integrative analysis of cancer omics data for prognosis modeling. Genes, 10(8), 604.

18.   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.

19.   Wu M, Ma S^* (2019). Robust genetic interaction analysis. Briefings in Bioinformatics, 20(2): 624-637

20.   Xu Y, Zhong T, Wu M^*, Ma S^* (2019). Histopathological imaging–environment interactions in cancer modeling. Cancers, 2019, 11(4): 579.

21.   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.

22. 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.

23.   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.

24. 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.

25.   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.

26. Wu M, Zhu L, Feng X^* (2018). Network-based feature screening with applications to genome data. The Annals of Applied Statistics, 12: 1250-1270.

27.  Wu M, Huang J, Ma S^* (2018). Identifying gene-gene interactions using penalized tensor regression. Statistics in Medicine, 37(4): 598-610.  

28. 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.

29.   Teran Hidalgo SJ, Wu M, Ma S^* (2017). Assisted clustering of gene expression data using ANCut. BMC Genomics, 18: 623.

30. 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.

31. 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: e66256.

32.   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: 2137–2153.

33.   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: 1649-1662.

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.