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.

Courses Taught in Other Semesters
Publications
    1. Hendricks AE, Dupuis J, Gupta M, Logue MW, Lunetta KL. A comparison of gene region simulation methods. PLoS One. 2012; 7(7):e40925.
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    2. Moser C, Gupta M. A Generalized Hidden Markov Model for Determining Sequence-based Predictors of Nucleosome Positioning. Stat Appl Genet Mol Biol. 2012; 11(2).
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    3. Gupta M, Cheung CL, Hsu YH, Demissie S, Cupples LA, Kiel DP, Karasik D. Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations. J Bone Miner Res. 2011 Jun; 26(6):1261-71.
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    4. Mitra R, Gupta M. A continuous-index Bayesian hidden Markov model for prediction of nucleosome positioning in genomic DNA. Biostatistics. 2011 Jul; 12(3):462-77.
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    5. Meltzer M, Long K, Nie Y, Gupta M, Yang J, Montano M. The RNA editor gene ADAR1 is induced in myoblasts by inflammatory ligands and buffers stress response. Clin Transl Sci. 2010 Jun; 3(3):73-80.
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    6. Gelfond JA, Gupta M, Ibrahim JG. A Bayesian hidden Markov model for motif discovery through joint modeling of genomic sequence and ChIP-chip data. Biometrics. 2009 Dec; 65(4):1087-95.
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    7. Cheng F, Hartmann S, Gupta M, Ibrahim JG, Vision TJ. A hierarchical model for incomplete alignments in phylogenetic inference. Bioinformatics. 2009 Mar 1; 25(5):592-8.
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    8. Gupta M, Ibrahim JG. An Information Matrix Prior for Bayesian Analysis in Generalized Linear Models with High Dimensional Data. Stat Sin. 2009; 19(4):1641-1663.
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    9. Gupta M. Model selection and sensitivity analysis for sequence pattern models. Inst Math Stat Collect. 2009 Jan 1; 1:390-407.
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    10. Jeong YC, Walker NJ, Burgin DE, Kissling G, Gupta M, Kupper L, Birnbaum LS, Swenberg JA. Accumulation of M1dG DNA adducts after chronic exposure to PCBs, but not from acute exposure to polychlorinated aromatic hydrocarbons. Free Radic Biol Med. 2008 Sep 1; 45(5):585-91.
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    11. Gupta M. Generalized hierarchical markov models for the discovery of length-constrained sequence features from genome tiling arrays. Biometrics. 2007 Sep; 63(3):797-805.
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    12. Gupta M, Qu P, Ibrahim JG. A temporal hidden Markov regression model for the analysis of gene regulatory networks. Biostatistics. 2007 Oct; 8(4):805-20.
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    13. Maki A, Kono H, Gupta M, Asakawa M, Suzuki T, Matsuda M, Fujii H, Rusyn I. Predictive power of biomarkers of oxidative stress and inflammation in patients with hepatitis C virus-associated hepatocellular carcinoma. Ann Surg Oncol. 2007 Mar; 14(3):1182-90.
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    14. Giresi PG, Gupta M, Lieb JD. Regulation of nucleosome stability as a mediator of chromatin function. Curr Opin Genet Dev. 2006 Apr; 16(2):171-6.
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    15. Gupta M, Liu JS. De novo cis-regulatory module elicitation for eukaryotic genomes. Proc Natl Acad Sci U S A. 2005 May 17; 102(20):7079-84.
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