Selected Preprints
Fan, K., Jiang, Y., Ma, S., Wang, W. and Wu, C.* (2024+). Robust Sparse Bayesian Regression for Longitudinal Gene-Environment Interactions. [mixedBayes]
Liu,Y.., Ren, J., Ma, S. and Wu, C.* (2024+). The Spike-and-Slab Quantile LASSO for robust variable selection in cancer genomics studies. [arXiv] [emBayes]
Lu,X.., Ren, J., Ma, S. and Wu, C.* (2024+). Bayesian quantile elastic net with spike-and-slab priors. [Bayenet]
Publications (current/former group members and corresponding author*)
Zhou, F., Ren, J., Ma, S. and Wu, C.* (2023). The Bayesian regularized quantile varying coefficient model. Computational Statistics & Data Analysis. 107808. (In press) [pqrBayes]
Ren,J., Zhou, F., Li, X., Ma, S., Jiang, Y. and Wu, C.* (2023). Robust Bayesian variable selection for gene-environment interactions. Biometrics. 79(2), 684-694. [Supplement] [arXiv] [roben]
Zhou, F., Liu,Y., Ren, J., Wang, W. and Wu, C.* (2023). Springer: an R package for bi-level variable selection of high-dimensional longitudinal data. Frontiers in Genetics.14:1088223. [springer]
Zhou, F., Lu, X., Ren, J., Fan, K., Ma, S. and Wu, C.* (2022). Sparse group variable selection for Gene-environment interactions in the longitudinal study. Genetic Epidemiology. 46(5-6), 317-340 [arXiv] [springer]
Zhou, F., Ren, J., Liu, Y., Li, X., Wang, W. and Wu, C*. (2022) Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data. Genes. 13(3), 544. [PDF] [interep]
Lu, X., Fan, K., Ren, J., and Wu, C.* (2021). Identifying Gene-environment interactions with robust marginal Bayesian variable selection. Frontiers in Genetics. 12:667074. [marble]
Quist, K. M., Solorzano, I., Wendel, S. O., Chintala, S., Wu, C., Wallace, N. A., and Katzenellenbogen, R. A. (2021). Cervical Cancer Development: Implications of HPV16 E6E7-NFX1-123 Regulated Genes. Cancers, 13(24), 6182. (Solorzano was a statistics undergraduate student in our group)
Zhou, F., Ren, J., Lu, X., Ma, S. and Wu, C.* (2021). Gene–Environment Interaction: a Variable Selection Perspective. Epistasis: Methods and Protocols. 191-223. Springer [Link] [arXiv]
Du, Y., Fan, K., Lu, X., Wu, C.* (2021). Integrating Multi–Omics Data for Gene-Environment Interactions. BioTech, 10(1), 3. [Link]
Wendel, S., Snow, J., Bastian, T., Brown, L., Hernandez, C., Burghardt, E., Kahn, A., Murthy, V., Neill, D., Smith, Z., Ault, K., Tawfik, O., Wu, C. and Wallace, N. (2021). High Risk α-HPV E6 Impairs Translesion Synthesis by Blocking POLη Induction. Cancers, 13(1), 28. (Taylor was an undergraduate student in statistics, and the recipient of the award for K State undergraduate research in the college of arts & sciences.)
Kasenda, B., Liu, J., Jiang, Y., Gajewski, B., Wu, C., von Elm, E., Schandelmaier, S., Moffa, G., Trelle, S., Schmitt, A.M., Herbrand, A.K., Gloy, V., Speich, B., Hopewell, S., Hemkens, L.G., Sluka, C., McGill, K., Meade, M., Cook, D., Lamontagne, F., Tréluyer, J.M., Haidich, A.B., Ioannidis, J., Treweek, S. and Briel, M. (2020). Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)—rationale and design for an international collaborative study. Trials, 21(1), 1-7. [Link]
Li, G., Hou, L., Liu, X., and Wu, C. (2020). A weighted empirical Bayes risk prediction model using multiple traits. Statistical Applications in Genetics and Molecular Biology. 19(3): 1–14. [Link]
Jung, L., Wang, H., Li, X., and Wu, C. (2020). A machine learning method for selection of genetic variants to increase prediction accuracy of type 2 diabetes mellitus using sequencing data. Statistical Analysis and Data Mining. 13(3): 261–281 [Link] (Luann is currently an undergraduate student in Computer Science, MIT class of 2022. She is also the coauthor of package regnet)
Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang,Y. and Wu, C.* (2020). Semiparametric Bayesian variable selection for Gene–Environment interactions. Statistics in Medicine. 39(5): 617–638 [arXiv] [spinBayes]
Zhou, F., Ren,J., Li, G., Jiang, Y., Li, X., Wang, W. and Wu, C.* (2019). Penalized Variable Selection for Lipid–Environment Interactions in a Longitudinal Lipidomics Study. Genes. 10(12), 1002 [PDF] [interep]
Satapathy, S., Jiang, Y., Agbim, U., Wu, C., Bernstein, D., Teperman, L., Kedia, S., Aithal, G., Bhamidimarri, K., Duseja, A., Maiwall, R., Maliakkal, B., Jalal, P., Patel, K., Puri, P., Ravinuthala, R., Wong, V., Abdelmalek, M., Ahmed, A., Thuluvath, P., Singal, A. on behalf of Global NAFLD Consortium. (2019). Post-transplant outcome of lean compared to obese nonalcoholic steatohepatitis in the United States: the obesity paradox. Liver Transplantation. (In press) [Link]
Wu, C.*, Zhou, F., Ren, J., Li, X., Jiang, Y. and Ma, S. (2019). A selective review of multi-level omics data integration using variable selection. High-Throughput. 8(1):1–25. [PDF] (Invited feature paper) Downloaded over 10K times.
Ren, J., Du, Y., Li, S., Ma, S., Jiang,Y. and Wu, C.* (2019). Robust network-based regularization and variable selection for high dimensional genomics data in cancer prognosis. Genetic Epidemiology. 43(3): 276–291. [regnet]
Wu, C., Zhang, Q., Jiang, Y. and Ma, S. (2018). Robust network-based analysis of the associations between (epi)genetic measurements. Journal of Multivariate Analysis. 168: 119–130
Wu, C., Zhong, P.-S. and Cui, Y. (2018). Additive varying-coefficient model for nonlinear gene-environment interactions. Statistical Applications in Genetics and Molecular Biology. 17(2) [PDF]
Wu, C.*, Jiang, Y., Ren, J., Cui, Y. and Ma, S. (2018). Dissecting gene-environment interactions: a penalized robust approach accounting for hierarchical structures. Statistics in Medicine. 37(3): 437–456.
Ren, J., He, T. , Li, Y., Liu, S., Du, Y., Jiang, Y. and Wu, C.* (2017). Network-based regularization for high dimensional SNP data in the case–control study of Type 2 diabetes. BMC Genetics. 18(1): 44 [regnet] (# Jie finished the version at initial submission when she was a master student; Ye was an undergraduate student)
Jiang, Y., Huang, Y., Du, Y., Zhao, Y., Ren, J., Ma, S. and Wu, C.* (2016). Identification of prognostic genes and pathways in lung adenocarcinoma using a Bayesian approach. Cancer Informatics . (accepted)
Wu, C., Shi, X., Cui, Y. and Ma, S. (2015) A penalized robust semiparametric approach for gene-environment interactions. Statistics in Medicine, 34 (30): 4016–4030
Krauthammer,K., Kong, Y., Evans,P., Bacchiocchi, A., Pornputtapong, N., Wu, C., McCusker, J., Ma, S., Cheng, E., Straub, R., Ariyan, S., Narayan, D., Sznol, M., Kluger, H., Mane, S. Lifton, R., Schlessinger, J. and Halaban,R. (2015) Exome sequencing identifies recurrent mutations in NF1 and RASopathy genes in sun-exposed melanomas. Nature Genetics, 47: 996–1002.
Wu, C. and Ma, S. (2015) A selective review of robust variable selection with applications in bioinformatics. Briefings in Bioinformatics. 16 (5): 873–883.
Heleski, C., Wickens C., Minero, M., DallaCosta, E., Wu, C., Czeszak, E. and Koenig von Borstel, U. (2015) Do soothing vocal cues enhance horses’ ability to learn a frightening task? Journal of Veterinary Behavior: Clinical Applications and Research, 10 (1): 41–47.
Wu, C., Cui, Y. and Ma, S. (2014) Integrative analysis of gene-environment interactions under a multi-response partially linear varying coefficient model. Statistics in Medicine, 33 (28): 4988–4498.
Wu, C. and Cui, Y. (2014) Boosting signals in gene set association studies via selective SNP profiling. Briefings in Bioinformatics, 15 (2): 279–291.
Wang, H., He, T., Wu, C., Zhong, P.S. and Cui, Y. (2014) A powerful statistical method identifies novel loci associated with diastolic blood pressure triggered by nonlinear GxE interaction. BMC Proceedings, 8 (Suppl 1):S61.
Wu, C. and Cui Y. (2013) A novel method for identifying nonlinear gene-environment interactions in case-control association studies. Human Genetics, 132 (12): 1413–1425.
Geu-Flores, F., Sherden, N.H., Courdavault, V., Burlat, V., Glenn, W.S., Wu, C., Nims, E., Cui, Y.H., and O'Connor, S.E. (2012) An alternative route to cyclic terpenes by reductive cyclization in iridoid biosynthesis. Nature. 492: 138–142.
Wu, C., Li, S. and Cui, Y. (2012) Genetic association studies: an information content perspective. Current Genomics, 13 (7): 566–573.
Li, G., Wu, C., Coelho, C., Wu, R., Larkins, B. and Cui, Y. (2012) A bivariate variance components model for mapping imprinted quantitative trait loci underlying endosperm traits. Frontiers in Bioscience (Elite Ed), 4: 2464–2475.
Wu, C., Li, G., Zhu, J. and Cui, Y. (2011) Functional mapping of dynamic traits with robust t-distribution. PLoS ONE, 6(9): e24902.