Selected publications
- Gupta, M. (2014).
An Evolutionary Monte Carlo Algorithm for Bayesian Block Clustering of Data
Matrices.
Comput. Stat. and Data Analy., 71, 375–391.
- 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.
- 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.
- 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.
- 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.
- 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]
- 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.
- 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.
- 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.
- 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]
- 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.
- 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.
- Gupta, M. (2007). Generalized hierarchical Markov
models for discovery of length-constrained sequence features from
genome tiling arrays. Biometrics, 63 (3): 797-805.
[PREPRINT]
- 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]
- 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., 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]
- 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 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.
- 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 Ibrahim, J. G. (2006). Bayesian methods for some missing
data problems in functional genomics. International Society for Bayesian
Analysis Bulletin, 13 (1): 6-10.
- 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.
-
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.
- 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
-
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. Software
- 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.