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2020 – today
- 2024
[j27]Seungjun Ahn
, Somnath Datta:
SOHPIE: statistical approach via pseudo-value information and estimation for differential network analysis of microbiome data. Bioinform. 40(1) (2024)- [j26]Seungjun Ahn, Somnath Datta:
Differential network connectivity analysis for microbiome data adjusted for clinical covariates using jackknife pseudo-values. BMC Bioinform. 25(1): 117 (2024) - 2023
- [j25]Seungjun Ahn, Tyler Grimes, Somnath Datta:
A pseudo-value regression approach for differential network analysis of co-expression data. BMC Bioinform. 24(1): 8 (2023) - 2022
- [j24]Mary Gregg, Somnath Datta, Douglas Lorenz:
htestClust: A Package for Marginal Inference of Clustered Data Under Informative Cluster Size. R J. 14(2): 54-66 (2022) - 2021
- [j23]Tyler Grimes, Somnath Datta:
SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data. J. Stat. Softw. 98(1) (2021)
2010 – 2019
- 2019
- [j22]Yichen Chen, Somnath Datta:
Adjustments of multi-sample U-statistics to right censored data and confounding covariates. Comput. Stat. Data Anal. 135: 1-14 (2019) - 2018
- [j21]Maiju Pesonen, Jaakko Nevalainen, Steven Potter, Somnath Datta, Susmita Datta:
A Combined PLS and Negative Binomial Regression Model for Inferring Association Networks from Next-Generation Sequencing Count Data. IEEE ACM Trans. Comput. Biol. Bioinform. 15(3): 760-773 (2018) - 2016
- [j20]Somnath Datta, María del Carmen Pardo, Thomas H. Scheike, Kam Chuen Yuen:
Special issue on advances in survival analysis. Comput. Stat. Data Anal. 93: 255-256 (2016) - 2015
- [j19]Maiying Kong, Sheng Xu, Steven M. Levy, Somnath Datta:
GEE type inference for clustered zero-inflated negative binomial regression with application to dental caries. Comput. Stat. Data Anal. 85: 54-66 (2015) - [j18]Luís Meira-Machado, Jacobo de Uña-Álvarez, Somnath Datta:
Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model. Comput. Stat. 30(2): 377-397 (2015) - 2014
- [j17]Jasmit S. Shah, Somnath Datta, Susmita Datta:
A multi-loss super regression learner (MSRL) with application to survival prediction using proteomics. Comput. Stat. 29(6): 1749-1767 (2014) - 2013
- [j16]Sutirtha Chakraborty, Somnath Datta, Susmita Datta:
svapls: an R package to correct for hidden factors of variability in gene expression studies. BMC Bioinform. 14: 236 (2013) - [j15]Farida Mostajabi, Somnath Datta, Susmita Datta:
Predicting Patient Survival from Proteomic Profile using Mass Spectrometry Data: An Empirical Study. Commun. Stat. Simul. Comput. 42(3): 485-498 (2013) - 2012
- [j14]Sutirtha Chakraborty, Somnath Datta, Susmita Datta:
Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies. Bioinform. 28(6): 799-806 (2012) - 2011
- [j13]Jie Fan, Somnath Datta:
Fitting marginal accelerated failure time models to clustered survival data with potentially informative cluster size. Comput. Stat. Data Anal. 55(12): 3295-3303 (2011) - 2010
- [j12]Ryan Gill, Somnath Datta, Susmita Datta:
A statistical framework for differential network analysis from microarray data. BMC Bioinform. 11: 95 (2010) - [j11]Susmita Datta, Vasyl Pihur, Somnath Datta:
An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data. BMC Bioinform. 11: 427 (2010)
2000 – 2009
- 2009
- [j10]Vasyl Pihur, Susmita Datta, Somnath Datta:
RankAggreg, an R package for weighted rank aggregation. BMC Bioinform. 10 (2009) - 2008
- [j9]Vasyl Pihur, Somnath Datta, Susmita Datta:
Reconstruction of genetic association networks from microarray data: a partial least squares approach. Bioinform. 24(4): 561-568 (2008) - 2007
- [j8]Vasyl Pihur, Susmita Datta, Somnath Datta:
Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach. Bioinform. 23(13): 1607-1615 (2007) - 2006
- [j7]Susmita Datta, Somnath Datta:
Evaluation of clustering algorithms for gene expression data. BMC Bioinform. 7(S-4) (2006) - [j6]Susmita Datta, Somnath Datta:
Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes. BMC Bioinform. 7: 397 (2006) - [c3]Grzegorz M. Boratyn, Susmita Datta, Somnath Datta:
Biologically Supervised Hierarchical Clustering Algorithms for Gene Expression Data. EMBC 2006: 5515-5518 - [c2]Susmita Datta, Somnath Datta:
Validation Measures for Clustering Algorithms Incorporating Biological Information. IMSCCS (1) 2006: 131-135 - 2005
- [j5]Susmita Datta, Somnath Datta:
Empirical Bayes screening of many p-values with applications to microarray studies. Bioinform. 21(9): 1987-1994 (2005) - 2004
- [j4]Susmita Datta, Glen A. Satten, Dale J. Benos, Jiazeng Xia, Martin J. Heslin, Somnath Datta:
An empirical bayes adjustment to increase the sensitivity of detecting differentially expressed genes in microarray experiments. Bioinform. 20(2): 235-242 (2004) - [j3]Glen A. Satten, Somnath Datta, Hercules Moura, Adrian R. Woolfitt, Maria da G. Carvalho, George M. Carlone, Barun K. De, Antonis Pavlopoulos, John R. Barr:
Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens. Bioinform. 20(17): 3128-3136 (2004) - [c1]Somnath Datta, Susmita Datta:
An Empirical Bayes Adjustment to Multiple p-values for the Detection of Differentially Expressed Genes in Microarray Experiments. APBC 2004: 155-159 - 2003
- [j2]Susmita Datta, Somnath Datta:
Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinform. 19(4): 459-466 (2003) - [p1]Glen A. Satten, Somnath Datta:
Marginal Analyses of Multistage Data. Advances in Survival Analysis 2003: 559-574 - 2000
- [j1]Glen A. Satten, Somnath Datta:
The S-U algorithm for missing data problems. Comput. Stat. 15(2): 243-277 (2000)
Coauthor Index
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