Publications

Published/In Press/Accepted

  1. Spencer, M., Takahashi, N., Chakraborty, S., Miles, J., and Shyu, C., (2018), "Heritable Genotype Contrast Mining Reveals Novel Gene Associations Specific to Autism Subgroups," Journal of the American Medical Informatics Association, 77, 50-61. Link

  2. Xu, C. and Chakraborty, S., (2017), "Bayesian Kernel Machine Models for Testing Genetic Pathway Effects in Prostate Cancer Prognosis," Statistical Analysis and Data mining, In press DOI: 10.1002/sam.11349. Link

  3. Kargupta, R, Puttaswamy, S, Lee, A, Butler, T, Chakraborty, S., Sengupta,S, (2017), "Rapid culture-based detection of living Mycobacteria using microchannel Electrical Impedance Spectroscopy (m-EIS)," Biological Research, 50, 21. Link

  4. Lee, K.H., Chakraborty, S., and Sun, J., (2017), "Variable Selection for High-Dimensional Genomic Data with Censored Outcomes Using Group Lasso Prior," Computational Statsitics and Data Analysis, 112, 1-13. Link

  5. Chakraborty, S. (2016), "Bayesian Additive Regression Tree for Seemingly Unrelated Regression with Automatic Tree Selection" Handbook of Statistics, Volume 35, Cognitive Computing: Theory and Applications, 229-251. Link

  6. Bhattacharya, S, Steele, R, Srivastava, S, Chakraborty, S, Bisceglie, A, and Ray,R.B, (2016), "Serum microRNA, miR-30e and miR-223, as novel non-invasive biomarkers for hepatocellular carcinoma," The American Journal of Pathology, 186:2,242-247. Link

  7. Roy, V. and Chakraborty, S., (2016), "Selection of Tuning Parameters, Solution Paths and Standard Errors for Bayesian Lassos," Bayesian Analysis, 12:3, 753-778. Link

  8. Lee, K.H., Chakraborty, S., and Sun, J., (2015) "Survival Prediction and Variable Selection with Simultaneous Shrinkage and Grouping Priors," Statistical Analysis and Data Mining, 8(2), 114-127. Link

  9. Liu, F., Chakraborty, S., Li, F., Liu, Y., and Lozano, A.C., (2014), "Bayesian Regularization via Graph Laplacian," Bayesian Analysis, 9:2, 449-474. Link

  10. Chakraborty, S., Mallick, B.K. , and Ghosh, M. (2013), "Bayesian Hierarchical Kernel Machines for Nonlinear Regression and Classification," invited chapter, inBayesian Theory and Applications (A tribute to Sir Adrian Smith), edited by Paul Damien, Petros Dellaportas, Nicholas G. Polson and David A. Stephens, Oxford University Press, 50-69. Link

  11. Chakraborty, S., Ghosh, M., and Mallick, B.K. (2012), "Bayesian Non Linear Regression for Large p Small n Problems," Journal of Multivariate Analysis, 108, 28–40. Link

  12. Chakraborty, S. (2012), "Multivariate Bayesian Kernel Regression Model for High Dimensional Data and its Practical Applications in Near-Infrared (NIR) Spectroscopy," Computational Statistics and Data Analysis, 56:9, 2742–2755. Link

  13. Chakraborty, S. and Ghosh, M. (2012), "Applications of Bayesian Neural Networks in Prostate cancer Study," Invited chapter in Handbook of Statistics, Volume 28: Bioinformatics, North Holland, 241-262. Link

  14. Puttaswamy, S., Lee, B.D., Amighi, B., Chakraborty, S., Sengupta, S. (2012), " Novel Electrical Method for the Rapid Determination of Minimum Inhibitory Con­centration (MIC) and Assay of Bactericidal/Bacteriostatic Activity," Journal of Biosensors and Bio­electronics, S2:003. Link

  15. Chakraborty, S. (2011), "Bayesian Semi-supervised Learning with Support Vector Machine," Statistical Methodology (special edition on data mining),8:1, 68-82. Link

  16. Chakraborty, S. and Guo, R. (2011), "A Bayesian Hybrid Huberized SVM and its Applications in High Dimensional Medical Data," Computational Statistics and Data Analysis, 55:3, 1342-1356. Link

  17. Lee, K.H., Chakraborty, S., and Sun, J. (2011), "Bayesian Variable Selection in Semiparametric

  18. Proportional Hazards Model for High Dimensional Survival Data," The International Journal of Biostatistics, 7:1, 1-32. Link

  19. Khalilia, M., Chakraborty, S., and Popescu, M. (2011), "Predicting Disease Risks from Highly Unbalanced Data Using Random Forest," BMC Medical Informatics and Decision Making, 11:51. Link

  20. Mallick, B.K., Chakraborty, S., and Ghosh, M. (2011), discussion paper of "Data Augmentation for Support Vector Machines, by Nicholas G. Polson and Steven L. Scott, " invited article in Bayesian Analysis, 6:1, 25-30. Link

  21. Guo, R. and Chakraborty, S. (2010), "Bayesian Adaptive Nearest Neighbor," Statistical Analysis and Data mining, 3:2, 92-105. Link

  22. Chakraborty, S, Holan, S., and, McElroy, T. (2009), "A Bayesian Approach to Estimating the Long Memory Parameter," Bayesian Analysis, 4:1, 159-190. Link

  23. Chakraborty, S. (2009), "Simultaneous Cancer Classification and Gene Selection with Bayesian Nearest Neighbor Method: An Integrated Approach," Computational Statistics and Data Analysis, 53:4, 1462-1474. Link

  24. Chakraborty, S. (2009), "Bayesian Binary Kernel Probit Model for Microarray Based Cancer Classification and Gene Selection," Computational Statistics and Data Analysis, 53:2, 4198-4209. Link

  25. Lin, G.N., Cai, Z., Lin, G., Chakraborty, S., and Xu, D. (2009), "ComPhy: Prokaryotic Composite Distance Phylogenies Inferred from Whole-Genome Gene Sets," BMC Bioinformatics, 10(Suppl 1):S5. Link

  26. Chakraborty, S., Ghosh, M., Mallick, B.K., Ghosh, D., and Dougherty, E. (2007), "Gene Expression-Based Glioma Classification Using Hierarchical Bayesian Vector Machines," Sankhya, 69:3, 514-547. Link

  27. Chakraborty, S., Ghosh, M., Maiti, T., and Tewari, A. (2005), "Bayesian Neural Networks for Bivariate Binary Data: An Application to Prostate Cancer Study," Statistics in Medicine, 24, 3645-3662. Link

  28. Ghosh, M., Maiti, T., Kim, D., Chakraborty, S., and Tewari, A. (2004), "Hierarchical Bayesian Neural Networks: An Application to a Prostate Cancer Study," Journal of the American Statistical Association,99:467, 601-608. Link

Invited Book Reviews

  1. Chakraborty, S. (2014), "Bayesian Essentials with R by Jean-Michel Marin and Christian P. Robert," Invited book review forInternational Statistical Review, 82(3), 488-490. Link

  2. Chakraborty, S. (2011), "An Intermediate Course in Probability by Allan Gut," Invited book review for Journal of the American Statistical Association, 106(495), 1221. Link

Book

  1. Bayesian Methods and Models in Machine Learning and Data Mining, by Sounak Chakraborty, CRC Press, Taylor and Francis Group, under contract, tentative date of delivary of manuscript Dec 2018.

Under Review

  1. Chakraborty, S. and Lozano, A.C., (2017), "Graph Laplacian Prior for Variable Selection and Grouping," Computational Statistics and Data Analysis, under review.

  2. Hoeksema, J.D., Bever, J.D., Chakraborty, S., Housworth, E., Lajeunesse, M., Meadow, J.F., Milligan, B., Rúa, M., Umbanhowar, and J., Viechtbauer, (2018), "Evolutionary history predicts the strength of mycorrhizal mutualism: A meta-analysis", Nature Communications Biology, under review.