Anne Laure Boulesteix

Summary

Country: Germany

Publications

  1. ncbi request reprint WilcoxCV: an R package for fast variable selection in cross-validation
    Anne Laure Boulesteix
    Sylvia Lawry Centre for Multiple Sclerosis Research, Hohenlindenerstr 1, D 81677 Munich, Germany
    Bioinformatics 23:1702-4. 2007
  2. ncbi request reprint PLS dimension reduction for classification with microarray data
    Anne Laure Boulesteix
    Department of Statistics, University of Munich
    Stat Appl Genet Mol Biol 3:Article33. 2004
  3. pmc Bias in random forest variable importance measures: illustrations, sources and a solution
    Carolin Strobl
    Institut für Statistik, Ludwig Maximilians Universitat Munchen, Ludwigstr, 33, 80539 Munchen, Germany
    BMC Bioinformatics 8:25. 2007
  4. ncbi request reprint Partial least squares: a versatile tool for the analysis of high-dimensional genomic data
    Anne Laure Boulesteix
    Department of Medical Statistics and Epidemiology, Technical University of Munich, Ismaningerstrasse 22, D 81675 Munich, Germany
    Brief Bioinform 8:32-44. 2007
  5. ncbi request reprint Maximally selected chi-square statistics and binary splits of nominal variables
    Anne Laure Boulesteix
    Department of Medical Statistics and Epidemiology, Technical University of Munich, Ismaningerstr 22, D 81675 Munich, Germany
    Biom J 48:838-48. 2006
  6. doi request reprint Use of pretransformation to cope with extreme values in important candidate features
    Anne Laure Boulesteix
    Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr 15, 81377 Munich, Germany
    Biom J 53:673-88. 2011
  7. doi request reprint Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations
    Anne Laure Boulesteix
    Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr 15, 81377 Munich, Germany
    Brief Bioinform 13:292-304. 2012
  8. ncbi request reprint Maximally selected chi-square statistics for ordinal variables
    Anne Laure Boulesteix
    Department of Statistics, University of Munich, Akademiestrasse 1, D 80799 Munich, Germany
    Biom J 48:451-62. 2006
  9. pmc Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction
    Anne Laure Boulesteix
    Department of Statistics, University of Munich, Ludwigstr 33, D 80539 Munich, Germany
    BMC Med Res Methodol 9:85. 2009
  10. doi request reprint Over-optimism in bioinformatics: an illustration
    Monika Jelizarow
    Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Munich, Germany
    Bioinformatics 26:1990-8. 2010

Collaborators

Detail Information

Publications25

  1. ncbi request reprint WilcoxCV: an R package for fast variable selection in cross-validation
    Anne Laure Boulesteix
    Sylvia Lawry Centre for Multiple Sclerosis Research, Hohenlindenerstr 1, D 81677 Munich, Germany
    Bioinformatics 23:1702-4. 2007
    ..This implementation is based on a simple mathematical formula using only the ranks calculated from the original data set...
  2. ncbi request reprint PLS dimension reduction for classification with microarray data
    Anne Laure Boulesteix
    Department of Statistics, University of Munich
    Stat Appl Genet Mol Biol 3:Article33. 2004
    ..In addition, we show how PLS can be used for data visualization using real data. The whole study is based on 9 real microarray cancer data sets...
  3. pmc Bias in random forest variable importance measures: illustrations, sources and a solution
    Carolin Strobl
    Institut für Statistik, Ludwig Maximilians Universitat Munchen, Ludwigstr, 33, 80539 Munchen, Germany
    BMC Bioinformatics 8:25. 2007
    ....
  4. ncbi request reprint Partial least squares: a versatile tool for the analysis of high-dimensional genomic data
    Anne Laure Boulesteix
    Department of Medical Statistics and Epidemiology, Technical University of Munich, Ismaningerstrasse 22, D 81675 Munich, Germany
    Brief Bioinform 8:32-44. 2007
    ..g. tumor classification from transcriptome data, identification of relevant genes, survival analysis and modeling of gene networks and transcription factor activities...
  5. ncbi request reprint Maximally selected chi-square statistics and binary splits of nominal variables
    Anne Laure Boulesteix
    Department of Medical Statistics and Epidemiology, Technical University of Munich, Ismaningerstr 22, D 81675 Munich, Germany
    Biom J 48:838-48. 2006
    ..Applications of the derived distribution to variable selection and hypothesis testing are discussed based on simulations. As an illustration, our method is applied to a birth data set...
  6. doi request reprint Use of pretransformation to cope with extreme values in important candidate features
    Anne Laure Boulesteix
    Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr 15, 81377 Munich, Germany
    Biom J 53:673-88. 2011
    ..The use of the transformation and its effects is demonstrated for diverse univariate and multivariate statistical analyses using nine publicly available microarray data sets...
  7. doi request reprint Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations
    Anne Laure Boulesteix
    Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr 15, 81377 Munich, Germany
    Brief Bioinform 13:292-304. 2012
    ..All our analyses can be reproduced using R code available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/ginibias/...
  8. ncbi request reprint Maximally selected chi-square statistics for ordinal variables
    Anne Laure Boulesteix
    Department of Statistics, University of Munich, Akademiestrasse 1, D 80799 Munich, Germany
    Biom J 48:451-62. 2006
    ..As an illustration, this method is applied to a new data set describing pregnancy and birth for 811 babies...
  9. pmc Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction
    Anne Laure Boulesteix
    Department of Statistics, University of Munich, Ludwigstr 33, D 80539 Munich, Germany
    BMC Med Res Methodol 9:85. 2009
    ..The focus of our work is on class prediction based on high-dimensional data (e.g. microarray data), since such analyses are particularly exposed to this kind of bias...
  10. doi request reprint Over-optimism in bioinformatics: an illustration
    Monika Jelizarow
    Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Munich, Germany
    Bioinformatics 26:1990-8. 2010
    ..So far, however, a systematic critical study concerning the various sources underlying this over-optimism is lacking...
  11. ncbi request reprint A CART-based approach to discover emerging patterns in microarray data
    Anne Laure Boulesteix
    Seminar for Applied Stochastics, Department of Statistics, University of Munich, Akademiestrasse 1, D 80799 Munich, Germany
    Bioinformatics 19:2465-72. 2003
    ..However, finding useful (i.e. short and statistically significant) EP is typically very hard...
  12. pmc Iterative reconstruction of high-dimensional Gaussian Graphical Models based on a new method to estimate partial correlations under constraints
    Vincent Guillemot
    Department of Medical Informatics, Biometry and Epidemiology of Faculty of Medicine, University of Munich, Munich, Germany
    PLoS ONE 8:e60536. 2013
    ..Plus, we show on simulated and real data that iPACOSE shows very interesting properties with regards to sensitivity, positive predictive value and stability...
  13. pmc Testing the additional predictive value of high-dimensional molecular data
    Anne Laure Boulesteix
    Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr 15, D 81377 Munich, Germany
    BMC Bioinformatics 11:78. 2010
    ....
  14. doi request reprint Added predictive value of high-throughput molecular data to clinical data and its validation
    Anne Laure Boulesteix
    University of Munich, Germany
    Brief Bioinform 12:215-29. 2011
    ....
  15. pmc An AUC-based permutation variable importance measure for random forests
    Silke Janitza
    Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr 15, D 81377, Munich, Germany
    BMC Bioinformatics 14:119. 2013
    ..In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance...
  16. doi request reprint Subsampling versus bootstrapping in resampling-based model selection for multivariable regression
    Riccardo De Bin
    Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr 15, 81377 Munich, Germany
    Biometrics 72:272-80. 2016
    ..We conduct our investigations by analyzing two real data examples and by performing a simulation study. Our results reveal some advantages in using a subsampling technique rather than the bootstrap in this context. ..
  17. pmc Conditional variable importance for random forests
    Carolin Strobl
    Department of Statistics, Ludwig Maximilians Universitat Munchen, Ludwigstrasse 33, D 80539 Munchen, Germany
    BMC Bioinformatics 9:307. 2008
    ..Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables...
  18. ncbi request reprint Reader's reaction to "Dimension reduction for classification with gene expression microarray data" by Dai et al (2006)
    Anne Laure Boulesteix
    Department of Medical Statistics and Epidemiology, Technical University of Munich, Germany
    Stat Appl Genet Mol Biol 5:Article16. 2006
    ..This note is a comment on the article "Dimension Reduction for Classification with Gene Expression Microarray Data" that appeared in Statistical Applications in Genetics and Molecular Biology (Dai et al., 2006)...
  19. doi request reprint Pitfalls of hypothesis tests and model selection on bootstrap samples: Causes and consequences in biometrical applications
    Silke Janitza
    Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr 15, 81377 Munich, Germany
    Biom J 58:447-73. 2016
    ..Moreover, we investigate the behavior of subsampling (i.e., drawing from a data set without replacement) as a potential alternative solution to the bootstrap for these procedures. ..
  20. doi request reprint Categorical variables with many categories are preferentially selected in bootstrap-based model selection procedures for multivariable regression models
    Susanne Rospleszcz
    Department of Medical Informatics, Biometry and Epidemology, University of Munich, Marchioninistr 15, 81377 Munich, Germany
    Biom J 58:652-73. 2016
    ..Importantly, variables with no effect and many categories may be (wrongly) preferred to variables with an effect but few categories. We suggest the use of subsamples instead of bootstrap samples to bypass these drawbacks. ..
  21. pmc Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach
    Anne Laure Boulesteix
    Department of Statistics, University of Munich, Ludwigstr 33, D 80539 Munich, Germany
    Theor Biol Med Model 2:23. 2005
    ..Unfortunately, with standard microarray experiments it is not possible to measure the transcription factor activities (TFAs) directly, as their own transcription levels are subject to post-translational modifications...
  22. doi request reprint Effects of aprotinin dosage on renal function: an analysis of 8,548 cardiac surgical patients treated with different dosages of aprotinin
    Wulf Dietrich
    Institute for Research in Cardiac Anesthesia, Munich, Germany
    Anesthesiology 108:189-98. 2008
    ..Recently, concerns about the safety of this drug were raised, especially regarding impaired renal outcome. This event rate was supposed to be dose dependent...
  23. doi request reprint Multiple testing for SNP-SNP interactions
    Anne Laure Boulesteix
    Sylvia Lawry Centre and Institute for Medical Statistics and Epidemiology, Technical University of Munich
    Stat Appl Genet Mol Biol 6:Article37. 2007
    ..An implementation of our method is available from the Comprehensive R Archive Network (CRAN) as R package 'SNPmaxsel'...
  24. doi request reprint Microarray-based prediction of tumor response to neoadjuvant radiochemotherapy of patients with locally advanced rectal cancer
    Caroline Rimkus
    Department of Surgery, Immunology and Hygiene, Klinikum rechts der Isar der Technischen Universitat Munchen, Munich, Germany
    Clin Gastroenterol Hepatol 6:53-61. 2008
    ..The aim of the study was to evaluate the capacity of gene expression signatures to identify responders and nonresponders pretherapeutically...
  25. doi request reprint Microarray-based classification and clinical predictors: on combined classifiers and additional predictive value
    Anne Laure Boulesteix
    Sylvia Lawry Centre for MS Research, Hohenlindenerstr 1, D 81677 Munich, Germany
    Bioinformatics 24:1698-706. 2008
    ..Moreover, they should address the question of the additional predictive value of microarray data in a fair framework...