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Xing Qiu, PhD

Xing Qiu, PhDAssociate Professor of Biostatistics and Computational Biology
Ph.D. (2004) University of Rochester

Contact Information

University of Rochester
Dept of Biostatistics and Computational Biology
265 Crittenden Boulevard, CU 420630 
Rochester, New York 14642-0630
Office: Saunders Research Building 4226
Phone: (585) 275-0666
Fax: (585) 273-1031

Research Interests

After I finished my PhD study in mathematics (research area: Stochastic Differential Equations) in 2004, I started working in the Department of Biostatistics and Computational Biology at the University of Rochester. My main research interests lie in the field of computational biology, especially microarray data analysis. Since 2004, I have studied the intergene correlation structure, normalizations, and stability of various gene selection procedures. My most recent research is on developing new statistical procedures which select differential expressed genes by utilizing their correlation with other genes.

I am also interested in the following research fields:

  • Theory of nonparametric statistics.
  • The theory and applications of stochastic differential equations, especially in modeling infectious disease.
  • Information geometry and its application to hypothesis testing procedures based on correlation/covariance.

I am also involved in several collaborative research projects. Most of them are related to either microarray data analysis or with the Department of Orthopaedics and Rehabilitation, where I hold a joint appointment.

Full List of Publications

Please visit my URMC Profile for a full list of my recent publications.

Selected Publications

  • Qiu, X., Hong, Y. and Shen, Y. (2001). An Improvement on the concentration-compactness principle. Acta Mathematicae Applicatae Sinica 17: 60-67.
  • Qiu, X., Brooks, A. I., Klebanov, L. and Yakovlev, A. (2005). The effects of normalization on the correlation structure of microarray data. BMC Bioinformatics 6: 120.
  • Qiu, X., Klebanov, L. and Yakovlev, A. (2005). Correlation Between Gene Expression Levels and Limitations of the Empirical Bayes Methodology for Finding Differentially Expressed Genes. Statistical Applications in Genetics and Molecular Biology 4: Article 34.
  • Qiu, X., Xiao, Y., Gordon, A. and Yakovlev, A. (2006). Assessing Stability of Gene Selection in Microarray Data Analysis. BMC Bioinformatics 7, Article 50.
  • Almudevar, A., Klebanov, L.B., Qiu, X., Salzman, P., and Yakovlev, A.Y. (2006). Utility of correlation measures in analysis of gene expression. NeuroRx 3(3): 384-395.
  • Qiu, X. and Yakovlev, A. (2006). Some comments on instability of false discovery rate estimation. J. Bioinformatics and Computational Biology 4 Article 5.
  • Klebanov, L., Qiu, X., Welle, S., and Yakovlev, A. (2007).  Statistical methods and microarray data. Nature Biotechnology 25(1): 25-26.
  • Qiu, X. and Yakovlev, A. (2007).  Comments on probabilistic models behind the concept of false discovery rate. Journal of Bioinformatics and Computational Biology5(4): 963-975.
  • Gordon, A., Glazko, G., Qiu, X. and Yakovlev, A. (2007).  Control of the Mean Number of False Discoveries, Bonferroni, and Stability of Multiple Testing. Annals of Applied Statistics, 1(1): 179-190.
  • Li, X., Nott, S.L., Huang, Y., Hilf, R., Bambara, R.A., Qiu, X., Yakovlev, A., Welle, S., and Muyan, M. (2008). Gene expression profiling reveals that the regulation of estrogen-responsive element-independent genes by 17beta-estradiolestrogen receptor beta is uncoupled from the induction of phenotypic changes in cell models. J Mol Endocrinol 40(5):211–229.
  • Wuertzer, C., Sullivan, M., Qiu, X., and Federoff, H. (2008).  CNS Delivery of Vectored Prion-specific Single-chain Antibodies Delays Disease Onset. Molecular Therapy 16(3):481-486.
  • Klebanov, L., Qiu, X. and Yakovlev, A. (2008).  Testing differential expression in non-overlapping gene pairs: A new perspective for the empirical Bayes method. J. Bioinformatics and Computational Biology 6(1):301–316.
  • Hu, R., Qiu, X., Glazko, G., Klebanov, L., and Yakovlev, A. (2009). Detecting intergene correlation changes in microarray analysis: A new approach to gene selection. BMC Bioinformatics, 10(20).
  • Qiu, X., Wu, S., and Wu, H. (2015). A new information criterion based on Langevin mixture distribution for clustering circular data with application to time course genomic data. Statistica Sinica 25(4):1459-1476.
  • Qiu X, Wu S, Hilchey SP, Thakar J, Liu ZP, Welle SL, Henn AD, Wu H, Zand MS. (2015). Diversity in Compartmental Dynamics of Gene Regulatory Networks: The Immune Response in Primary Influenza A Infection in Mice. PLOS ONE. 10(9):e0138110.
  • Liu B, Qiu X, Zhu T, Tian W, Hu R, Ekholm S, Schifitto G, Zhong J. (2016). Spatial regression analysis of serial DTI for subject-specific longitudinal changes of neurodegenerative disease. NeuroImage: Clinical. 11:291-301.