Selected publications


  1. Cheng, F., Hartmann, S., Gupta, M., Ibrahim, J. G., and Vision, T. J. (2009). A hierarchical model for incomplete alignments in phylogenetic inference. Bioinformatics, (Advance Access).

  2. 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).

  3. 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, (Advance Access).

  4. 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. May 15 [Epub ahead of print].

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

  6. 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. [PDF]

  7. 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.

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

  9. 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. [PDF]

  10. 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.

  11. 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.

  12. 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.

  13. 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): 610.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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

  19. 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.