Boston University School of Public Health Directory

Mayetri Gupta


Associate Professor, Biostatistics
Biostatistics

Harvard University, PhD
Indian Statistical Institute, MS




Office: Crosstown Center, CT312
Phone: (617) 414-7946
Email:

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Courses Taught:
BS 850 (Spring 2009): Advanced Statistical Methodology for the Computational Biosciences
 
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Biography:

Mayetri Gupta joined the Biostatistics department in September 2007. Previously she was an Assistant Professor in the Department of Biostatistics and the Center for Genome Sciences at the University of North Carolina (UNC) at Chapel Hill. Her research focus is at the interface of Statistics and Computational Biology, addressing high-dimensional and missing data problems in genomics and bioinformatics, especially relating to gene regulation. Her methodological work includes the development of several novel statistical methods for the discovery of transcription factor binding sites from DNA sequence in a variety of genomes, ranging from bacteria to human. More recently, she has been involved in the development of Bayesian methodologies for the discovery of gene regulatory networks combining multiple genomic data types, and discovery and analysis of chromatin features from high throughput data from genome tiling arrays. Her other statistical research interests include Bayesian model and variable selection, Bayesian methods for clustering and classification and inference in hidden Markov and other latent class models. For more information about Dr.Gupta's work, please visit http://people.bu.edu/gupta/. Dr. Gupta also serves as a Bioinformatics mentor for the Biostatistics Training Grant in the department of Biostatistics.


 

Recent Publications:


Gelfond, J. L., Gupta, M. and Ibrahim, J. G. (2008). A Bayesian hidden Markov model for jointly modeling probe sequences and ChIP-chip data for motif discovery. Biometrics, in press.

Gupta, M. and Ibrahim, J. G. (2008). An information matrix prior for Bayesian analysis in generalized linear models with high dimensional data. Statistica Sinica, in press.

Gupta, M. (2007). Model selection and sensitivity analysis for sequence pattern models. Beyond Parametrics in Interdisciplinary Research: a festschrift in honour of Prof. P. K. Sen, Lecture Notes series of the IMS, in press.

Zhou, Q. and Gupta, M. (2007). Regulatory Motif Discovery– from Decoding to Meta-Analysis. Frontiers of Statistics, 1, in press.

Gupta, M. (2007). Generalized hierarchical Markov models for discovery of length-constrained sequence features from genome tiling arrays. Biometrics, 63 (3): 797-805.

Gupta, M., Qu, P. and Ibrahim, J. G. (2007). Hidden Markov regression models for the analysis of temporal gene regulatory networks. Biostatistics, 8: 805-820.

Gupta, M. and Ibrahim, J. G. (2007). Variable selection in regression mixture modeling for the discovery of gene regulatory networks.  Journal of the American Statistical Association, 102 (479), 867-880.

Maki, A., Kono, H., Gupta, M., Asakawa, M., Suzuki, T., Matsuda, M., Fujii, H., Rusyn, I. (2007). Predictive power of biomarkers of oxidative stress and inflammation in patients with hepatitis C virus-associated hepatocellular carcinoma. Annals of Surgical Oncology, 14:1182-1190.

Gupta, M. and Liu, J. S. (2006).  Bayesian modeling and inference for motif discovery. in Bayesian inference for gene expression and proteomics, Do et al., (eds). Cambridge University Press.

Giresi, P.G., Gupta, M. and Lieb, J. D. (2006). Regulation of nucleosome stability as a mediator of chromatin function. Curr. Opin. Genet. Dev. 16 (2):171-176.   

Gupta, M. and Ibrahim, J. G. (2006). Bayesian methods for some missing data problems in functional genomics. International Society for Bayesian Analysis Bulletin, 13 (1): 6–10.

Gelfond, J. L. and Gupta, M. (2006). Bayesian models for motif discovery from ChIP-chip and sequence data. International Society for Bayesian Analysis Bulletin, 13 (4): 2–4.

Gupta, M. and Ray, S. (2006). Sequence pattern discovery with applications to understanding gene regulation and vaccine design. in Handbook of Statistics, C. R. Rao and R. Chakraborty (eds.), Elsevier Press.

Gupta, M. and Liu, J. S. (2005).  De-novo cis-regulatory module elicitation for eukaryotic genomes. Proceedings of the National Academy of Sciences of the USA, 102 (20): 7079–7084.

Gupta, M. and Liu, J. S. (2004).  Discussion on  “A Bayesian approach to DNA sequence segmentation'' by R. J. Boys and D. A.  Henderson, Biometrics, 60 (3): 573-844. 

Gupta, M. and Liu, J. S. (2003).  Discovery of conserved sequence patterns using a stochastic dictionary model. Journal of the American Statistical Association, 98 (461): 55-66.

Liu,  J. S., Gupta, M. , Liu, X. S., Lawrence, C. L. (2002).  Statistical models for motif discovery. Case Studies in Bayesian Statistics, Vol. 6, Springer-Verlag, New York.