Riten Mitra, PhD

Riten MitraBioinformatics and Biostatistics 
Associate Professor 
Room No.122, 485 E. Gray St.
Louisville, KY 40202
Phone: 502-852-3986
Fax: 502-852-3294

r0mitr01@louisville.edu

 Background Information
I completed my undergraduate and master’s from Indian Statistical Institute, Kolkata. I earned my PhD in Biostatistics from the University of North Carolina at Chapel Hill in 2010.  I was a postdoctoral fellow in  the Biostatistics department at the MD Anderson Cancer Center  from 2010-12 and at the University of Texas Austin from 2012-13. I joined University of Louisville in fall 2013. My research is primarily centered around Bayesian hierarchical models. Applications include biological  networks, high throughput genomics, and high dimensional clinical data. Recently I have developed an interest in the paradigm of stochastic computational  biology and have started working at the  intersection of stochastic differential equations and kernel regression.

Research Interests
My primary research interests are in Bayesian Graphical Models and developing hierarchical latent variable techniques for next generation sequencing data. I am currently focussing on network inference problems in epigenetics (histone modifications) and high throughput proteomics. I also have a strong interest in NP-Bayes clustering algorithms. Recently I have been involved in formulating NP-Bayes models for subgroup analysis and clinical trials.

PUBLICATIONS

Kundu, D., Mitra, R., Gaskins, JT.   Bayesian variable selection for multioutcome models through shared shrinkage. Scandinavian Journal of Statistics, 2019.

Pal, S., Sengupta, S., Mitra, R., Banerjee, A.  Conjugate Priors and Posterior Inference for the Matrix Langevin Distribution on the Stiefel Manifold. BayesianAnalysis, 2018.

Owolabi, U.S., Amraotkar, A.R., Coulter, A.R., Singam, N.S.V., Aladili, B.N., Singh, A., Trainor, P.J., Mitra, R. and DeFilippis, A.P. Change in Matrix Metalloproteinase 2, 3, and 9 Levels at the Time of and After Acute Atherothrombotic Myocardial Infarction. Journal of Thrombosisand Thrombolysis, 2019.

Mitra, R., Gill, R., Sikdar, S., & Datta, S.  Bayesian Hierarchical Model for Protein Identifications. Journal of Applied Statistics, Volume 46, Number 1, 30-46, 2019.

Bandara,U., Gill, R., Mitra R. On computing maximum likelihood estimates for the negative binomial distribution. Statistics and Probability letters, 148: 54-5, 2019.

Liu, R., Gaskins, JT., Mitra, R., Wu, D. .A Review of Estimation of Key Parameters and Lead Time in Cancer Screening. Revista Colombiana deEstadstica , Volume 40, Issue 2, pp. 263 to 278, 2017.

Mitra, R., Mueller, P.,  Ji, Y.  Bayesian Multiplicity Control for Multiple Graphs. Canadian Journal of Statistics, Volume 45, Number 1, 44-61,2017.

Mitra, R., Mueller, P.,   Ji, Y.  Bayesian graphical models for differential pathways. Bayesian Analysis, Volume 11, Number 1, 99-124, 2016.

Zhu, Y. , Xu, Y. , Helseth, D., Gulukota, K., Yang, S., Pesce, L., Mitra, R. Mueller, P. Sengupta, S. Guo, W., Silverstein, J., Foster, I. , Parsad, N. , White, K. and Ji, Y. Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data Journal of National Cancer Institute, Volume 107, Number 8, 2015.

Mitra R., Mueller P., Ji, Y., Zhu, Y., Mills, G., Lu, Y. A Bayesian Hierarchical Model for Inference Across Related RPPA Experiments. Journal of Applied Statistics,Volume 41, Number 11, 2483-2492, 2014.

Mitra R., Muller P., Ji Y., Zhu Y., Peng, Q. Bayesian Hierarchical Models for Protein Networks in Single Cell Mass Cytometry. Cancer informatics 13.Suppl 4,79-89, 2013.

Mueller P. and Mitra R. Bayesian Nonparametric Inference: Why and How? Bayesian analysis (Online) 8.2, 2013.

Mitra, R., Mueller, P., Liang, S., Yue, L. , Ji, Y., A Bayesian graphical model for ChIP- Seq data on Histone Modifications. Journal of American Statistical Association 108: 69-80, 2013.

Mitra R., Mueller P., Liang S., Xu Y., Ji Y. Towards Breaking the Histone Code: Bayesian Graphical Models for Histone Modifications. Circulation: Cardiovascular Genetics, 2013.

Xu Y., Lee J., Yuan Y., Mitra R., Liang S., Mueller P., Ji Y. Nonparametric Bayesian Bi-Clustering for ChIP-Seq Count Data. Bayesian Analysis. 8(2):1-22, 2013.

Mitra, R., Mueller, P., Ji, Y. Propriety conditions for the Bayesian autologistic model Journal of Statistical Theory and Practice. 7(2):248-258, 2013.

Xu, Y., Zhang, J., Yuan, Y., Mitra, R., Mueller, P.,  Ji, Y.  A Bayesian Graphical Model for Integrative Analysis of TCGA Data.  IEEE International Workshop on GenomicSignal Processing and Statistics, 2012.

Mitra, R., Mueller, P., Ji, Y., Mills, G., Lu, Y. Sparse Bayesian Graphical Models for RPPA Time Course Data.  IEEE International Workshop on Genomic Signal Processing and Statistics, 2012.

Ji, Y., Mitra, R., Quintana, F., Jara, A., Mueller, P., Liu, P., Lu, Y. and Liang, S.  BM-BC : Bayesian base calling for Solexa sequence data. BMC Bioinformatics. 13 Suppl 13:S6, 2012.

Mitra, R., Gupta, M. A continuous-index Bayesian hidden Markov model for prediction of nucleosome positioning in genomic DNA.  Biostatistics. 12, 3 ,462–477, 2011.

Book Chapters

Lorenz, D., Gill, R., Mitra, R., and Datta, S. Statistical Analysis of next generation sequencing data: An overview. In Statistical analysis of next generation sequencing data. (Eds., Somnath Datta and Dan Nettleton). Springer-Verlag, 2014.

Mitra, R. and Mueller, P.  Bayesian Hierarchical Models for ChipSeq data. In Statistical analysis of next generation sequencing data.(Eds., Somnath Datta and Dan Nettleton). Springer-Verlag, 2014.

Mitra, R., Mueller, P., Ji, Y.  Bayesian Model-Based Approaches for Solexa Sequencing data. In Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput Data. (Eds., Do K. A., Qin Z. S. and Vannucci M.) Cambridge University Press, 2013.

 

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