Publications

Statistical Theory for Bayesian and Computation-heavy Methods

• Chakraborty, S. and Su, Z. (2023). A comprehensive Bayesian framework for envelope models. Journal of the American Statistical Association (Theory and Methods), pp.1-11. Link.
• Shen Y, Park Y, Chakraborty S, Zhang C. (2023). Bayesian simultaneous partial envelope model with application to an imaging genetics analysis. The New England Journal of Statistics in Data Science. 1(2). pp. 237–269. Link.
• Mukherjee S, Khare K, and Chakraborty S. (2023). Convergence properties of data augmentation algorithms for high-dimensional robit regression. *Electronic Journal of Statistics. 17(1): 19-69 (2023). Link.
• Lee, M., Chakraborty, S., and Su, Z. (2022). A Bayesian approach to envelope quantile regression. Statistica Sinica. 32 (2022), 1-19 Link.
• Chakraborty, S. and Wong, S. W. (2022). On the circular correlation coefficients for bivariate von Mises distributions on a torus. Stat Papers. Link. arXiv.
• Chakraborty, S., Bhattacharya, B., and Khare, K. (2022). Estimating accuracy of the MCMC variance estimator: asymptotic normality for batch means estimators. Statistics and Probability Letters. 183, 109337. arXiv. 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
• 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.

Statistical Methodology in Computational Bio-medicine

• Chakraborty, S., Guan, Z., Kostrezawa, C.E., Shen R., and Begg C.B. (2024). Identifying Somatic Fingerprints of Cancers Defined by Germline and Environmental Risk Factors. To appear in Genetic Epidemiology.
• Chakraborty, S., Guan, Z., Begg, C.B., and Shen, R. (2024). Topical Hidden Genome: Discovering Latent Cancer Mutational Topics using a Bayesian Multilevel Context-learning Approach. To appear in Biometrics. arXiv.
• Chakraborty, S., Liu, A., Ball, R., and Markatou, M. (2022). On the Use of the Likelihood Ratio Test Methodology in Pharmacovigilance. Statistics in Medicine. 1(27), pp.5395-5420. Link.
• 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.

Computational Methodology

• 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.
• Chakraborty, S., and Markatou, M. (2024). Likelihood Ratio Test Based Drug Safety Assessment using R package pvLRT. Accepted for Publication in the R Journal.

Statistical Software Development

• BAMBI: An R package for Bivariate Angular Mixture Models. Downloaded over 40,000 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 genomic data, fitting and predicting from hidden genome classifiers.
• pvLRT: An R package for likelihood ratio test based methods for pharmacovigilance.

Collaborative Applied Research

• Rosi-Schumacher, M., DiNardo, L. A., Reese, A. D., Gupta, S., Nagy, R. E., Chakraborty, S., and Carr, M. M. (2024). Comparison of Surgical Techniques for the Treatment of Congenital Nasal Pyriform Aperture Stenosis: A Systematic Review. Annals of Otology, Rhinology & Laryngology, 00034894241242179. Link.

• Roeder, N M; Penman, S L; Richardson, B J; Wang, J; Freeman-Striegel, L; Khan, A; Pareek, O; Weiss, M; Mohr, P; Eiden, R D; Chakraborty, S; Thanos, P K. (2024). Vaporized Δ9-THC in utero results in reduced birthweight, increased locomotion, and altered wake-cycle activity dependent on dose, sex, and diet in the offspring. Life Sciences: 122447. doi: 10.1109/TUFFC.2023.3283139. Link.

• Huang, C; Cheng, Y; Zheng, W; Bing, R; Zhang, H; Komornichi, I; Harris, L; Arany, P; Chakraborty, S; Zhou, Q; Xu, W; Xia, J. (2023). Dual-Scan Photoacoustic Tomography for the Imaging of Vascular Structure on Foot. To appear in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

• 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 (2022). BMC Cardiovascular Disorders 22(1) pp. 1-12.

• Atanasova, K, Chakraborty, S (co-first author), Ratnayake, R, Khare, K, Luesch, H. (2022). An epigenetic small molecule screen to target abnormal nuclear morphology in human cells. To appear in Molecular Biology of the Cell.

• 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.

Book Chapter

• Chakraborty, S. Genome-driven cancer site characterization: an overview of the hidden genome model. (2023). To appear in Modern inference based on health-related markers - Biomarkers and Statistical Decision Making. Elsevier Inc.

Submitted or In-Revision Articles

• Hua, S.$^*$, Chakraborty, S. Inferring cancer-type-specific latent mutation signatures using a Bayesian tree-based supervised topic model.

• Huang, X.$^*$, Chakraborty, S. A Bayesian framework for medical product safety assessment using correlated spontaneous reporting system data.

• Tan, Y.$^*$, Chakraborty, S., Markatou, M. On the use of non-parametric empirical Bayesian approaches in Pharmacovigilance.

• Wang, S., Chakraborty, S., Qin, Q., Bai, R. Neural-g: A Deep Learning Framework for Mixing Density Estimation.

• Chakraborty, S., Khare, K, Michailidis, G. A generalized Bayesian approach for high-dimensional robust regression with serially correlated errors and predictors.

• Liu, A., Markatou, M., Chakraborty, S., and Ball, R. A pattern discovery algorithm for pharmacovigilance signal detection.

$^*:$ Ph.D. Advisee