Publications

Refereed Publications

Statistics in Genomics and Computational Biology

• Chakraborty, S., Martin, A., Guan, Z., Begg, C. B., and Shen, R. (2021). Mining mutation contexts across the cancer genome to map tumor site of origin. Nat Commun 12, 3051. Link.
• Chakraborty, S., Ecker, B. L., Seier, K., Aveson, V. G., Balachandran, V. P., Drebin, J. A., D’Angelica, M. I., Kingham, T. P., Sigel, C. S., Soares, K. C., Vakiani, E., Wei, A. C., Chandwani, R., Gonen, M., Shen, R., Jarnagin, W. R. (2021). Genome-derived Classification Signature for Ampullary Adenocarcinoma to Improve Clinical Cancer Care. Clinical Cancer Research. (27) (21) 5891-5899. Link.
• Chakraborty, S. Tian, L, Tseng, Y, and Wong, S. W. (2021). Bayesian analysis of coupled cellular and nuclear trajectories for cell migration. Biometrics 1-12. Link.
• Chakraborty, S., Begg, C. B., and Shen, R. (2020). Using the “Hidden” Genome to Improve Classification of Cancer Types. Biometrics. 2020;1–11. Link. arXiv.
• Chakraborty, S., Arora A., Begg, C. B. and Shen, R. (2019). Using Somatic Variant Richness to Mine Signals from Rare Variants in the Cancer Genome. Nat Commun 10, 5506 (2019). Link.

Markov Chain Monte Carlo Theory

• Chakraborty, S., Bhattacharya, B., and Khare, K. (2022). Estimating accuracy of the MCMC variance estimator: asymptotic normality for batch means estimators. To appear in Statistics and Probability Letters. arXiv.
• Chakraborty, S. and Khare, K. (2019). Consistent estimation of the spectrum of trace class data augmentation algorithms. Bernoulli. 25(4B), 2019, 3832–3863. arXiv. Link.
• Chakraborty, S. and Khare, K. (2017). Convergence properties of Gibbs samplers for Bayesian probit regression with proper priors, Electronic Journal of Statistics 11, 177-210. Link.

Multivariate Analysis and Robust Statistical Methods

• Lee, M., Chakraborty, S., and Su, Z. (2021). A Bayesian approach to envelope quantile regression. To appear in Statistica Sinica. Link.
• Maji, A., Chakraborty, S., and Basu, A., (2017). Statistical Inference based on the Logarithmic Power Divergence. Society For Application Of Statistics And Allied Sciences, 2, 39–51. Link

Computational Research

• Chakraborty, S. and Wong, S. W. (2021). BAMBI: An R package for Fitting Bivariate Angular Mixture Models. Journal of Statistical Software, 99(11), 1–69. Link.

Collaborative Applied Research

• Ambruster, C.E., Brauer, A.L., Humby, M.S., Shao, J., Chakraborty, S. (2021). Prospective assessment of catheter-associated bacteriuria in nursing home residents: clinical presentation, epidemiology, and colonization dynamics. JCI Insight. Oct 8;6(19):e144775. Link.

• Cassidy, D. J., Chakraborty, S., Panda, N., McKinley, S. K., Mansur, A., Hamdi I., Mullen, J., Petrusa, E., Phitayakorn, R., and Gee, D. (2020). The Surgical Knowledge “Growth Curve”: Predicting ABSITE Scores and Identifying “At-Risk” Residents. Journal of Surgical Education. Link.

• Barnard, A. M., Wilcox, R., Forbes, S.C., Daniels, M. J., Chakraborty, S., Lott, D., J., Senesac, C. R., Arora, H., Sweeny, L., Walter, G. H., and Vandenborne, K. H. E. (2020). MR biomarkers predict clinical function in Duchenne muscular dystrophy. Neurology, 94(9), e897-e909. Link.

• Rooney, W. D., Berlow, Y. A., Triplett, W. T., Forbes, S. C., Willcocks, R. J., Wang, D, Arora, H, Senesac, C, Lott, D. J., Finkel, R., Russman, B. S., Finanger, E. L., Chakraborty, S., O’Brien, E, Moloney, B, Barnard, A, Sweeney, H. L., Daniels, M. J., Walter, G. A., and Vandenborne, K. (2020). Modeling disease trajectory in Duchenne muscular dystrophy. Neurology, 94(15), e1622-e1633. Link.

• Vaziri, S., Awan, O., Porche, K., Scott, K., Sacks, P., Dru, A. B., Chakraborty, S., Khare, K., Hoh, B., and Rahman, M. (2019). Reimbursement Patterns for Neurosurgery: Analysis of the NERVES Survey Results from 2011-2016. Clinical Neurology and Neurosurgery, p.105406. Link.

• Chatterjee, N., Nair, P.K.R., Chakraborty, S., and Nair, V.D. (2018). Changes in soil carbon stocks across the Forest-Agroforest-Agriculture/Pasture continuum in various agroecological regions: A meta-analysis. Agriculture, Ecosystems and Environment, 266, 55-67. Link.

• Vaziri, S., Wilson, J., Abbatematteo, J., Kubilis, P., Chakraborty, S., Kshitij, K., and Hoh, D. J. (2017). Predictive performance of the American College of Surgeons universal risk calculator in neurosurgical patients. Journal of Neurosurgery, 1-6. Link.

Software

• BAMBI: An R package for Bivariate Angular Mixture Models. Downloaded over 35,000 times.
• variantprobs: An R package for estimating probabilities and expected numbers of mutations in the tumor genome.
• hidgenclassifier: An R package implementing Key functions for Bayesian hidden genome classifiers. Includes functions for preprocessing genommic data, fitting and predicting from hidden genome classifiers.
• pvLRT: An R package for likelihood ratio test based methods for pharmacovigilance.

Submitted or in-revision

• Mukherjee S, Khare K, and Chakraborty S. (2021+). Convergence properties of data augmentation algorithms for high-dimensional robit regression. Submitted.
• Chakraborty, S., and Markatou, M. (2021+). Likelihood Ratio Test Based Drug Safety Assessment using R package pvLRT.
• Chakraborty, S., Liu, A., Ball, R., and Markatou, M. (2021+). On the Use of the Likelihood Ratio Test Methodology in Pharmacovigilance. Submitted.
• Chakraborty, S. and Su, Z. (2021+). A comprehensive Bayesian framework for envelope models. In revision.
• Chakraborty, S. and Wong, S. W. (2021+) On the circular correlation coefficients for bivariate von Mises distributions on a torus. Under review. arXiv.
• Atanasova, K, Chakraborty, S (co-first author), Ratnayake, R, Khare, K, Luesch, H. (2021+). An epigenetic small molecule screen to target abnormal nuclear morphology in human cells. In revision.
• Batra, A, Barnard, A, Lott, D J, Willcocks, R, Forbes, S C, Chakraborty, S, Daniels, M, Arbogast, J, Triplett, W, Henricson, E, Dayan, J G, Schmalfuss, C, Sweeney, L, Byrne, B J, McDonald, C, Vandenborne, K, Walter, G A. Longitudinal changes in cardiac function in Duchenne muscular dystrophy population as measured by magnetic resonance imaging. (2021+) In revision.