Yuehua Cui is a professor in the Statistics and Probability Department of Michigan State University. Dr. Cui earned his Ph.D. at the University of Florida. His research interests are focused on developing novel statistical methods and computational tools in genetic/genomic data analysis. Areas of research include: gene-gene interactions, gene-environment interactions, genome-wide association studies, gene network inference, heterogeneous omics data integration, causal mediation analysis and longitudinal data analysis. His research has been funded by NSF and NIH. He has been collaborating with faculty in various fields such as genetics, psychology, nursing and family medicine.
Some recent publications:
- Yang, H.T., H. Cao, T. He, T. Wang and Y.H. Cui. (2018+) Multi-level heterogeneous omics data integration with kernel fusion. Briefings in Bioinformatics (accepted)
- He, T., S. Li, P-S. Zhong and Y.H. Cui. (2018+) An optimal kernel-based method for gene set association analysis. Genetic Epidemiology (in press)
- Wu, C., P-S. Zhong and Y.H. Cui. (2018) Additive varying-coefficient model for nonlinear gene-environment interactions. Statistical Applications in Genetics and Molecular Biology 17(2).
- Yang, H., S. Li, H. Cao, C. Zhang and Y.H. Cui. (2017) Predicting disease trait with genomic data: A composite kernel approach. Briefings in Bioinformatics 18(4): 591–601.
- Liu, X., Y.H. Cui and R. Li. (2016) Partial linear varying multi-index coefficient model for integrative gene-environment interactions. Statistica Sinica 26: 1037-1060.
- Gao, B. and Y.H. Cui. (2015) Learning directed acyclic graphical structures with genetical genomics data. Bioinformatics 31(24): 3953-3960.
- STT855 (Intro to Statistical Genetics);
- STT843 (Multivariate analysis); STT863 (Applied linear regression);
- STT864 (Generalized linear model)
- Our work on Gene-centric genetic association study (Cui et al. 2008, Genetics) was highlighted in Nature Reviews Genetics Vol. 9 No. 6 (2008).
With the recent radical breakthroughs in biotechnology, large amount of high throughput genetic/genomic data can now be generated almost without limit, with an ultimate goal of unraveling the hidden genetic secrets of biological functions. Well motivated by real world biological problems, my lab focuses on developing novel statistical models to help biologists to better understanding gene functions and enhance our knowledge about the molecular processes. We are looking for students who have strong statistical and computational backgrounds and who would like to dedicate their future research into this cutting-edge interdisciplinary area. Interested students should contact the graduate director first for a chance to enroll into the statistics program.
- Currently we are looking for highly motivated graduate students who are interested in statistical learning (a link to SL) and deep learning with applications in omics data analysis, with the hope to understand the molecular machinery of complex diseases from a system biology perspective.