We develop biostatistical methods and bioinformatics tools for analyzing medical data, specially, genomic, proteomic, multistate survival (time to event) and dental data.
Susmita Datta, PhD
Somnath Datta, PhD
@ University of Florida
@ University of Louisville
Post Doctoral Associate
A Combined PLS and negative binomial regression model for inferring association networks from next-generation sequencing count data.
Pesonen, M., Nevalainen, J., Potter, S. S, Datta, S., Datta, S. - IEEE/ACM Transactions on Computational Biology and Bioinformatics (2017).
Inter-platform concordance of gene expression data for the prediction of chemical mode of action. Siriwardhana, C., Datta, S., and Datta, S. - Biology Direct (2016).
Exploring the importance of cancer pathways by meta-analysis of differential protein expression networks in three different cancers. Sikdar, S., Datta, S., and Datta, S. Biology Direct (2016).
Cluster adjusted regression for displaced subject data (CARDS): marginal inference under potentially informative temporal cluster size profiles.
Bible, J., Beck, J. D., and Datta, S. - Biometrics (2016).
A novel rank-sum test for clustered data when the number of subjects in a group within a cluster is informative. Dutta, S. and Datta, S. - Biometrics (2016).
Marginal regression models for clustered count data based on zero-inflated Conway-Maxwell-Poisson distribution with application to the Iowa Fluoride Study. Choo-Wosoba, H., Levy, S. M., and Datta, S. - Biometrics (2016).
An integrative exploratory analysis of –omics data from the ICGC cancer genomes lung adenocarcinoma study. Sikdar, S., Choo Wosoba, H., Abdia, Y., Dutta, S., Gill, R., Datta, S., and Datta, S. - Systems Biomedicine, 2, 56-64 (2014).
dna: an R package for differential network analysis. Gill, R., Datta, S. and Datta, S. - Bioinformation, 10, 233-234 (2014).
svapls: An R package to correct for residual expression heterogeneity in gene expression data. Chakraborty, S., Datta, S. and Datta, S. - BMC Bioinformatics, 14, 236 (2013).
Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies. Chakraborty, S., Datta, S. and Datta, S. - Bioinformatics (2012).
Comparisons and validation of statistical clustering techniques for microarray gene expression data, S Datta, S Datta - Bioinformatics, 2003 (Citation count: 362, Google Scholar, 1/17)
Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes,S Datta, S Datta - BMC Bioinformatics, 2006 (Citation count: 148, Google Scholar, 1/17)
Empirical Bayes screening of many p-values with applications to microarray studies, S Datta, S Datta - Bioinformatics, 2005 (Citation count: 52, Google Scholar, 11/26/15)
clValid, an R package for cluster validation, G. Brock, V Pihur, S Datta, S Datta - Journal of Statistical Software, 2008 (Citation count: 234, Google Scholar, 1/17)
Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach, V Pihur, S Datta, S Datta - Bioinformatics, 2007 (Citation count: 100, Google Scholar, 1/17)
RankAggreg, an R package for weighted rank aggregation
V Pihur, S Datta, S Datta - BMC bioinformatics, 2009 (Citation count: 152, Google Scholar, 1/17)
Reconstruction of genetic association networks from microarray data: a partial least squares approach, V. Pihur, S Datta, S Datta - Bioinformatics, 2008 (Citation count: 45, Google Scholar, 1/17)