Selected publications


  1. Gupta, M. (2014). An Evolutionary Monte Carlo Algorithm for Bayesian Block Clustering of Data Matrices. Comput. Stat. and Data Analy., 71, 375–391.

  2. Lin, H., Gupta, M. et al. CHARGE Atrial Fibrillation Working Group. (2014). Targeted sequencing in candidate genes for atrial fibrillation: the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Targeted Sequencing Study. Heart Rhythm, 11 (3), 452-7.

  3. Gelfond, J. A. L., Ibrahim, J. G., Gupta, M., Chen, M. H., and Cody, J. D. (2013). Differential expression analysis with global network adjustment. BMC Bioinformatics, 14: 258.

  4. Moser, C., and Gupta, M. (2012). A generalized hidden Markov model for determining sequence-based predictors of nucleosome positioning. Stat Appl Genet Mol Biol. 11 (2), Art 2.

  5. Hendricks, A. E., Dupuis, J., Gupta, M., Logue, M. W., and Lunetta, K. L. (2012). A comparison of gene region simulation methods. PLoS One, 7 (7), e40925.

  6. Mitra, R., and Gupta M. (2011). A continuous-index Bayesian hidden Markov model for prediction of nucleosome positioning in genomic DNA. Biostatistics, 12 (3) 462-77. [PREPRINT]

  7. Gupta, M., Cheung, C.L., Hsu, Y.H., Demissie, S., Cupples, L.A., Kiel, D.P., and Karasik, D. (2011). Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of Genome-wide associations. Journal of Bone and Mineral Research, 26 (6), 1261-71.

  8. Meltzer, M., Long, K., Nie, Y., Gupta, M., Yang, J., and Montano, M. (2010). The RNA editor gene ADAR1 is induced in myoblasts by inflammatory ligands and buffers stress response. Clin Transl Sci. 3(3):73-80.

  9. Cheng, F., Hartmann, S., Gupta, M., Ibrahim, J. G., and Vision, T. J. (2009). A hierarchical model for incomplete alignments in phylogenetic inference. Bioinformatics, 25(5):592-8.

  10. Gupta, M. and Ibrahim, J. G. (2009). An information matrix prior for Bayesian analysis in generalized linear models with high dimensional data. Statistica Sinica, 19, 1641-1663. [PREPRINT]

  11. Gelfond, J. L., Gupta, M. and Ibrahim, J. G. (2009). A Bayesian hidden Markov model for motif discovery through joint modeling of genomic sequence and ChIP-chip data. Biometrics, 65(4):1087-95.

  12. Jeong, Y. C., Walker, N. J., Burgin, D. E., Kissling, G., Gupta, M., Kupper, L., Birnbaum, L. S., Swenberg, J. A. (2008). Accumulation of M(1)dG DNA adducts after chronic exposure to PCBs, but not from acute exposure to polychlorinated aromatic hydrocarbons. Free Radic Biol Med. 45(5):585-91.

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

  14. 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. [PREPRINT]

  15. 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.

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

  17. Gupta, M., Qu, P. and Ibrahim, J. G. (2007). A temporal hidden Markov regression model for the analysis of gene regulatory networks. Biostatistics, 8: 805-820. [PREPRINT]

  18. 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.

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

  20. 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.

  21. 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.

  22. Maki, A., Kono, H., Gupta, M. , Asakawa, M., Suzuki, T., Matsuda, M., Fujii, H., Rusyn, I. (2006). 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.

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

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

  25. 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.

  26. 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. Software

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