Lev Klebanov

Summary

Publications

  1. pmc Multivariate search for differentially expressed gene combinations
    Yuanhui Xiao
    Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642, USA
    BMC Bioinformatics 5:164. 2004
  2. ncbi request reprint Testing differential expression in nonoverlapping gene pairs: a new perspective for the empirical Bayes method
    Lev Klebanov
    Department of Probability and Statistics, Charles University, Sokolovska 83, Praha 8, CZ 18675, Czech Republic
    J Bioinform Comput Biol 6:301-16. 2008
  3. pmc Revisiting adverse effects of cross-hybridization in Affymetrix gene expression data: do they matter for correlation analysis?
    Lev Klebanov
    Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, Box 630, New York 14642, USA
    Biol Direct 2:28. 2007
  4. ncbi request reprint A new type of stochastic dependence revealed in gene expression data
    Lev Klebanov
    Department of Probability and Statistics, Charles University
    Stat Appl Genet Mol Biol 5:Article7. 2006
  5. pmc A nitty-gritty aspect of correlation and network inference from gene expression data
    Lev B Klebanov
    Department of Probability and Statistics, Charles University, Sokolovska 83, Praha 8, CZ 18675, Czech Republic
    Biol Direct 3:35. 2008
  6. ncbi request reprint A multivariate extension of the gene set enrichment analysis
    Lev Klebanov
    Department of Probability and Statistics, Charles University, Sokolovska 83, Praha 8, CZ 18675, Czech Republic
    J Bioinform Comput Biol 5:1139-53. 2007
  7. pmc The effects of normalization on the correlation structure of microarray data
    Xing Qiu
    Department of Biostatistics and Computational Biology, University of Rochester, New York 14642, USA
    BMC Bioinformatics 6:120. 2005
  8. pmc Detecting intergene correlation changes in microarray analysis: a new approach to gene selection
    Rui Hu
    Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA
    BMC Bioinformatics 10:20. 2009
  9. doi request reprint Aggregation effect in microarray data analysis
    Linlin Chen
    School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY, USA
    Methods Mol Biol 972:177-91. 2013
  10. doi request reprint Gene selection with the δ-sequence method
    Xing Qiu
    Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA
    Methods Mol Biol 972:57-71. 2013

Collaborators

Detail Information

Publications13

  1. pmc Multivariate search for differentially expressed gene combinations
    Yuanhui Xiao
    Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642, USA
    BMC Bioinformatics 5:164. 2004
    ..Such tests are essentially univariate and disregard the multidimensional structure of microarray data. A more general two-sample hypothesis is formulated in terms of the joint distribution of any sub-vector of expression signals...
  2. ncbi request reprint Testing differential expression in nonoverlapping gene pairs: a new perspective for the empirical Bayes method
    Lev Klebanov
    Department of Probability and Statistics, Charles University, Sokolovska 83, Praha 8, CZ 18675, Czech Republic
    J Bioinform Comput Biol 6:301-16. 2008
    ..The new paradigm arising from the existence of the delta-sequence in biological data offers considerable scope for future developments in this area of methodological research...
  3. pmc Revisiting adverse effects of cross-hybridization in Affymetrix gene expression data: do they matter for correlation analysis?
    Lev Klebanov
    Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, Box 630, New York 14642, USA
    Biol Direct 2:28. 2007
    ..The work by Okoniewski and Miller drove us to revisit the issue by means of experimentation with biological data and probabilistic modeling of cross-hybridization effects...
  4. ncbi request reprint A new type of stochastic dependence revealed in gene expression data
    Lev Klebanov
    Department of Probability and Statistics, Charles University
    Stat Appl Genet Mol Biol 5:Article7. 2006
    ..The ability to identify genes that act as ;;modulators'' provides a potential strategy of prioritizing candidate genes...
  5. pmc A nitty-gritty aspect of correlation and network inference from gene expression data
    Lev B Klebanov
    Department of Probability and Statistics, Charles University, Sokolovska 83, Praha 8, CZ 18675, Czech Republic
    Biol Direct 3:35. 2008
    ....
  6. ncbi request reprint A multivariate extension of the gene set enrichment analysis
    Lev Klebanov
    Department of Probability and Statistics, Charles University, Sokolovska 83, Praha 8, CZ 18675, Czech Republic
    J Bioinform Comput Biol 5:1139-53. 2007
    ..We also discuss some other aspects of the GSEA paradigm and suggest new avenues for future research...
  7. pmc The effects of normalization on the correlation structure of microarray data
    Xing Qiu
    Department of Biostatistics and Computational Biology, University of Rochester, New York 14642, USA
    BMC Bioinformatics 6:120. 2005
    ..A potential impact of between-gene correlations on the performance of such methods has yet to be explored...
  8. pmc Detecting intergene correlation changes in microarray analysis: a new approach to gene selection
    Rui Hu
    Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA
    BMC Bioinformatics 10:20. 2009
    ..We intend to enrich the above procedure by proposing a nonparametric selection procedure that selects differentially correlated genes...
  9. doi request reprint Aggregation effect in microarray data analysis
    Linlin Chen
    School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY, USA
    Methods Mol Biol 972:177-91. 2013
    ..A critical discussion of such pitfalls is long overdue. Here we discuss one feature of microarray data the investigators need to be aware of when embarking on a study of putative associations between elements of networks and pathways...
  10. doi request reprint Gene selection with the δ-sequence method
    Xing Qiu
    Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA
    Methods Mol Biol 972:57-71. 2013
    ..Furthermore, its outcomes are entirely free from the log-additive array-specific technical noise. The new paradigm offers considerable scope for future developments in this area of methodological research...
  11. pmc Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype
    Helene R McMurray
    Department of Biomedical Genetics, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, New York 14642, USA
    Nature 453:1112-6. 2008
    ....
  12. ncbi request reprint Treating expression levels of different genes as a sample in microarray data analysis: is it worth a risk?
    Lev Klebanov
    Stat Appl Genet Mol Biol 5:Article9. 2006
    ..This dependence represents a very serious pitfall in microarray data analysis...
  13. ncbi request reprint Statistical methods and microarray data
    Lev Klebanov
    Nat Biotechnol 25:25-6; author reply 26-7. 2007