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Susmita Datta
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2020 – today
- 2020
- [j19]Michael Sekula
, Jeremy T. Gaskins
A sparse Bayesian factor model for the construction of gene co-expression networks from single-cell RNA sequencing count data. BMC Bioinform. 21(1): 361 (2020)
2010 – 2019
- 2018
- [j18]Maiju Pesonen
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) - 2017
- [j17]Sinjini Sikdar, Susmita Datta:
A novel statistical approach for identification of the master regulator transcription factor. BMC Bioinform. 18(1): 79:1-79:11 (2017) - 2016
- [j16]Sinjini Sikdar, Ryan Gill, Susmita Datta:
Improving protein identification from tandem mass spectrometry data by one-step methods and integrating data from other platforms. Briefings Bioinform. 17(2): 262-269 (2016) - 2014
- [j15]Jasmit S. Shah
A multi-loss super regression learner (MSRL) with application to survival prediction using proteomics. Comput. Stat. 29(6): 1749-1767 (2014) - 2013
- [j14]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) - [j13]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
- [j12]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
- [j11]Juliet Ndukum, Luís L. Fonseca
Statistical Inference Methods for Sparse Biological Time Series Data. BMC Syst. Biol. 5: 57 (2011) - 2010
- [j10]Ryan Gill, Somnath Datta, Susmita Datta:
A statistical framework for differential network analysis from microarray data. BMC Bioinform. 11: 95 (2010) - [j9]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
- [j8]Vasyl Pihur, Susmita Datta, Somnath Datta:
RankAggreg, an R package for weighted rank aggregation. BMC Bioinform. 10 (2009) - 2008
- [j7]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
- [j6]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
- [j5]Susmita Datta, Somnath Datta:
Evaluation of clustering algorithms for gene expression data. BMC Bioinform. 7(S-4) (2006) - [j4]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
- [j3]Susmita Datta, Somnath Datta:
Empirical Bayes screening of many p-values with applications to microarray studies. Bioinform. 21(9): 1987-1994 (2005) - 2004
- [j2]Susmita Datta, Glen A. Satten
An empirical bayes adjustment to increase the sensitivity of detecting differentially expressed genes in microarray experiments. Bioinform. 20(2): 235-242 (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
- [j1]Susmita Datta, Somnath Datta:
Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinform. 19(4): 459-466 (2003)
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