Susmita Datta

Summary

Affiliation: University of Louisville
Country: USA

Publications

  1. ncbi Statistical inference methods for sparse biological time series data
    Juliet Ndukum
    Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40202, USA
    BMC Syst Biol 5:57. 2011
  2. ncbi Modeling microRNA-mRNA interactions using PLS regression in human colon cancer
    Xiaohong Li
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA
    BMC Med Genomics 4:44. 2011
  3. ncbi Computational biology touches all bases
    Susmita Datta
    Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40292, USA
    Genome Biol 10:303. 2009
  4. ncbi Fetal alcohol syndrome (FAS) in C57BL/6 mice detected through proteomics screening of the amniotic fluid
    Susmita Datta
    Department of Bioinformatics and Biostatistics, School of Public Health and Information Science, University of Louisville, Louisville, Kentucky 40292, USA
    Birth Defects Res A Clin Mol Teratol 82:177-86. 2008
  5. ncbi Evaluation of clustering algorithms for gene expression data
    Susmita Datta
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA
    BMC Bioinformatics 7:S17. 2006
  6. ncbi Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes
    Susmita Datta
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA
    BMC Bioinformatics 7:397. 2006
  7. ncbi Biologically supervised hierarchical clustering algorithms for gene expression data
    Grzegorz M Boratyn
    Kidney Disease Program and Clinical Proteomics Center, University of Louisville, KY, USA
    Conf Proc IEEE Eng Med Biol Soc 1:5515-8. 2006
  8. ncbi Reconstruction of genetic association networks from microarray data: a partial least squares approach
    Vasyl Pihur
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40292, USA
    Bioinformatics 24:561-8. 2008
  9. ncbi RankAggreg, an R package for weighted rank aggregation
    Vasyl Pihur
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA
    BMC Bioinformatics 10:62. 2009
  10. ncbi Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach
    Vasyl Pihur
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA
    Bioinformatics 23:1607-15. 2007

Collaborators

  • Vasyl Pihur
  • William M Pierce
  • Xiaohong Li
  • Grzegorz M Boratyn
  • Thomas B Knudsen
  • Jae Keun Yoo
  • Stuart Macgregor
  • P M Visscher
  • Jonathan Flint
  • Nigel M Williams
  • Patrick F Sullivan
  • G A Satten
  • D Curtis
  • Susanne Warrenfeltz
  • Vinay Puri
  • Juliet Ndukum
  • Ryan Gill
  • Jennifer L Stone
  • Jacob Lawrence
  • Andrew McQuillin
  • Khalid Choudhury
  • Nicholas Bass
  • Digby Quested
  • Robert Krasucki
  • Jonathan Pimm
  • Srinivasa Thirumalai
  • Gerrit J Schut
  • Daniela Steinberger
  • Eberhard O Voit
  • Luis L Fonseca
  • Helena Santos
  • Antonio Macedo
  • Helena Medeiros
  • Gillian Fraser
  • Aiden Corvin
  • Douglas H R Blackwood
  • Ben Pickard
  • Draga Toncheva
  • James A Knowles
  • Ivan Nikolov
  • Soh Leh Kwan
  • Emma F Thelander
  • Edward M Scolnick
  • Steve A McCarroll
  • Carlos Paz Ferreira
  • Vihra Milanova
  • Lucy Georgieva
  • Michael J Owen
  • Douglas M Ruderfer
  • Paul Lichtenstein
  • Elaine Kenny
  • David St Clair
  • Margaret Van Beck
  • M Helena Azevedo
  • Michele T Pato
  • N Norton
  • Peter A Holmans
  • Nick J Craddock
  • Alan W Maclean
  • Pat Malloy
  • Casey Gates
  • George K Kirov
  • David Conti
  • Hugh Gurling
  • Walter J Muir
  • Christina M Hultman
  • Mark Daly
  • H Williams
  • Celia Carvalho
  • Christopher Morley
  • Carlos N Pato
  • Michael Gill
  • Kevin A McGhee
  • Ayman Fanous
  • Frank Middleton
  • Kimberly Chambert
  • Mark J Daly
  • Derek W Morris
  • Pamela Sklar
  • Caroline Crombie
  • Joshua Korn
  • SHAUN M PURCELL
  • John L Waddington
  • Nicholas Walker
  • Helen Moorey
  • Katie Kelly
  • Simon Kerwin
  • Bhaskar Punukollu
  • Graham Lamb
  • Haitham Nadeem

Detail Information

Publications23

  1. ncbi Statistical inference methods for sparse biological time series data
    Juliet Ndukum
    Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40202, USA
    BMC Syst Biol 5:57. 2011
    ..For the statistical analysis we first fit several nonlinear mixed effect regression models to the longitudinal profiles and then used an ANOVA likelihood ratio method in order to test for significant differences between the profiles...
  2. ncbi Modeling microRNA-mRNA interactions using PLS regression in human colon cancer
    Xiaohong Li
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA
    BMC Med Genomics 4:44. 2011
    ....
  3. ncbi Computational biology touches all bases
    Susmita Datta
    Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40292, USA
    Genome Biol 10:303. 2009
    ..A report of the 6th Annual Rocky Mountain Bioinformatics Conference, Aspen, USA, 4-7 December 2008...
  4. ncbi Fetal alcohol syndrome (FAS) in C57BL/6 mice detected through proteomics screening of the amniotic fluid
    Susmita Datta
    Department of Bioinformatics and Biostatistics, School of Public Health and Information Science, University of Louisville, Louisville, Kentucky 40292, USA
    Birth Defects Res A Clin Mol Teratol 82:177-86. 2008
    ..The present study used this murine model to screen amniotic fluid for biomarkers that could potentially discriminate between FAS-positive and FAS-negative pregnancies...
  5. ncbi Evaluation of clustering algorithms for gene expression data
    Susmita Datta
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA
    BMC Bioinformatics 7:S17. 2006
    ..A closely related problem is that of selecting a clustering algorithm that is "optimal" in some sense from a rather impressive list of clustering algorithms that currently exist...
  6. ncbi Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes
    Susmita Datta
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA
    BMC Bioinformatics 7:397. 2006
    ..Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species...
  7. ncbi Biologically supervised hierarchical clustering algorithms for gene expression data
    Grzegorz M Boratyn
    Kidney Disease Program and Clinical Proteomics Center, University of Louisville, KY, USA
    Conf Proc IEEE Eng Med Biol Soc 1:5515-8. 2006
    ..R-codes of the clustering algorithm are available from the authors upon request...
  8. ncbi Reconstruction of genetic association networks from microarray data: a partial least squares approach
    Vasyl Pihur
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40292, USA
    Bioinformatics 24:561-8. 2008
    ..Here, we introduce a novel computational method based on the partial least squares (PLS) regression technique for reconstruction of genetic networks from microarray data...
  9. ncbi RankAggreg, an R package for weighted rank aggregation
    Vasyl Pihur
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA
    BMC Bioinformatics 10:62. 2009
    ..One of the major strengths of rank-based aggregation is the ability to combine lists coming from different sources and platforms, for example different microarray chips, which may or may not be directly comparable otherwise...
  10. ncbi Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach
    Vasyl Pihur
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA
    Bioinformatics 23:1607-15. 2007
    ..An automated and objective way of reconciling the rankings is needed...
  11. ncbi A statistical framework for differential network analysis from microarray data
    Ryan Gill
    Department of Mathematics, University of Louisville, Louisville, KY 40292, USA
    BMC Bioinformatics 11:95. 2010
    ....
  12. ncbi An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data
    Susmita Datta
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA
    BMC Bioinformatics 11:427. 2010
    ....
  13. ncbi Feature selection and machine learning with mass spectrometry data
    Susmita Datta
    Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, USA
    Methods Mol Biol 593:205-29. 2010
    ..In this chapter, we provide a review of major contributions toward feature selection and classification of proteomic mass spectra involving MALDI-TOF and SELDI-TOF technology...
  14. ncbi Finding common genes in multiple cancer types through meta-analysis of microarray experiments: a rank aggregation approach
    V Pihur
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40292, USA
    Genomics 92:400-3. 2008
    ....
  15. ncbi Predicting patient survival from microarray data by accelerated failure time modeling using partial least squares and LASSO
    Susmita Datta
    Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky 40202, USA
    Biometrics 63:259-71. 2007
    ..This reanalysis using the mean imputed PLS and LASSO identifies a number of genes that were known to be related to cancer or tumor activities from previous studies...
  16. ncbi An empirical bayes adjustment to increase the sensitivity of detecting differentially expressed genes in microarray experiments
    Susmita Datta
    Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, USA
    Bioinformatics 20:235-42. 2004
    ..Dudoit et al. have advocated use of permutation-based step-down P-value adjustments to correct the observed significance levels for the individual (i.e. for each gene) two sample t-tests...
  17. ncbi Gene expression profiling of epithelial ovarian tumours correlated with malignant potential
    Susanne Warrenfeltz
    Genetics Department, University of Georgia, Athens Georgia 30602, USA
    Mol Cancer 3:27. 2004
    ..Improved knowledge of gene expression changes and functional pathways associated with these clinical phenotypes may lead to new treatment targets, markers for early detection and a better understanding of disease progression...
  18. ncbi Empirical Bayes screening of many p-values with applications to microarray studies
    Susmita Datta
    Department of Mathematics and Statistics, Department of Biology, Georgia State University, Atlanta, 30303, USA
    Bioinformatics 21:1987-94. 2005
    ..AVAILABILITY: R code for EBS is available from the authors upon request. SUPPLEMENTARY INFORMATION: http://www.stat.uga.edu/~datta/EBS/supp.htm..
  19. ncbi Comparisons and validation of statistical clustering techniques for microarray gene expression data
    Susmita Datta
    Department of Mathematics and Statistics and Department of Biology, Georgia State University, Atlanta, GA 30303, USA
    Bioinformatics 19:459-66. 2003
    ..At the moment there do not seem to be any clear-cut guidelines regarding the choice of a clustering algorithm to be used for grouping genes based on their expression profiles...
  20. ncbi Genetic mapping of variation in spatial learning in the mouse
    Daniela Steinberger
    Wellcome Trust Centre for Human Genetics, Oxford, OX3 7BN, United Kingdom
    J Neurosci 23:2426-33. 2003
    ..QTL that influence differences in fearful behavior (on chromosomes 1, 3, 7, 15, and 19) operate while mice are trained in the water maze apparatus...
  21. ncbi Whole-genome DNA microarray analysis of a hyperthermophile and an archaeon: Pyrococcus furiosus grown on carbohydrates or peptides
    Gerrit J Schut
    Department of Biochemistry and Molecular Biology and Center for Metalloenzyme Studies, University of Georgia, Athens, Georgia 30602, USA
    J Bacteriol 185:3935-47. 2003
    ..The degree of coordinate regulation revealed by the microarray data was unanticipated and shows that P. furiosus can readily adapt to a change in its primary carbon source...
  22. ncbi Rare chromosomal deletions and duplications increase risk of schizophrenia
    Jennifer L Stone
    Nature 455:237-41. 2008
    ..Our results provide strong support for a model of schizophrenia pathogenesis that includes the effects of multiple rare structural variants, both genome-wide and at specific loci...
  23. ncbi Failure to confirm allelic association between markers at the CAPON gene locus and schizophrenia in a British sample
    Vinay Puri
    Molecular Psychiatry Laboratory, Department of Mental Health Sciences, Royal Free and University College London Medical School, Windeyer Institute of Medical Sciences, London, W1T 4JF, UK
    Biol Psychiatry 59:195-7. 2006
    ..A second Chinese study found a base pair polymorphism at the CAPON gene also associated with schizophrenia...