Institute for medical information - processing, biometry, and epidemiology (IBE)

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Prof. Dr. Anne-Laure Boulesteix

Submitted methodological papers/technical reports - statistics/methodology (6)

  1. B. Bischl, M. Binder, M. Lang, T. Pielok, J. Richter, S. Coors, J. Thomas, T. Ullmann, M. Becker, A.-L. Boulesteix, D. Deng, M. Lindauer, 2021. Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges.
  2. C. Nießl, M. Herrmann, C. Wiedemann, G. Casalicchio, A.-L. Boulesteix, 2021. Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results.
  3. R. Hornung, A.-L. Boulesteix, 2021. Interaction Forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects. Technical Report 237, Department of Statistics, LMU Munich..
  4. T. Ullmann, C. Hennig, A.-L. Boulesteix, 2021. Validation of cluster analysis results on validation data: A systematic framework.
  5. S. Klau, F. Schönbrodt, C. Patel, J. Ioannidis, A.-L. Boulesteix, S. Hoffmann, 2020. Comparing the vibration of effects due to model, data pre-processing and sampling uncertainty on a large data set in personality psychology. Technical Report 232, Department of Statistics, LMU. PsyArXiv. June 10. doi:10.31234/*contributed equally to this work.
  6. I. van Mechelen, A.-L. Boulesteix, R. Dangl, N. Dean, I. Guyon, C. Hennig, F. Leisch, D. Steinley, 2018. Benchmarking in cluster analysis: a white paper.

Reviewed book chapters (3)

  1. W. Sauerbrei, A.-L. Boulesteix, 2020. Model building and stability. In: "Principles and Practice of Clinical Trials". Eds: S. Piantadosi, C.L. Meinert. Publisher: Springer.
  2. U. Mansmann, A.-L. Boulesteix, 2020. Modelling individual response to treatment and its uncertainty: a review of statistical methods and challenges for future research. In: "Uncertainty in Pharmacology Epistemology, Methods, and Decisions''. Eds: A. LaCaze, B. Osimani. Publisher: Springer. pp319-344.
  3. A.-L. Boulesteix, R. Hornung, W. Sauerbrei, 2017. On fishing for significance and statistician’s degree of freedom in the era of big molecular data. In: "Ott, Max; Pietsch, Wolfgang; Wernecke, Jörg. Berechenbarkeit der Welt? Philosophie und Wissenschaft im Zeitalter von Big Data. Wiesbaden: Springer VS", pp 155-170 . R-codes to reproduce the results.

Reviewed manuscripts in international indexed journals (119)

  1. H. Seibold, A. Charlton, A.-L. Boulesteix, S. Hoffmann, 2021. Statisticians, roll up your sleeves! There's a crisis to be solved. Significance 18(4):42-44.
  2. S. Buchka, A. Hapfelmeier, P.P. Gardner, R. Wilson, A.-L. Boulesteix, 2021. On the optimistic performance evaluation of newly introduced bioinformatic methods. Genome Biology 22:152.
  3. S. Hoffmann, F. Schönbrodt, R. Elsas, R. Wilson, U. Strasser, A.-L. Boulesteix, 2021. The multiplicity of analysis strategies jeopardizes replicability: lessons learned across disciplines. Royal Society Open Science 8:201925.
  4. M. Herrmann, P. Probst, R. Hornung, V. Jurinovic, A.-L. Boulesteix, 2021. Large-scale benchmark study of survival prediction methods using multi-omics data. Briefings in Bioinformatics 22(3):1-15.
  5. S. Peschel, C.L. Müller, E. von Mutius, A.-L. Boulesteix, M. Depner, 2021. NetCoMi: Network Construction and comparison for microbiome data in R. Briefings in Bioinformatics 22(4):1-18.
  6. K. Lackermair, S. Brunner, M. Orban, S. Peterss, M. Orban, H.D. Theiss, B.C. Huber, G. Juchem, F. Born, A.-L. Boulesteix, A. Bauer, M. Pichlmaier, J. Hausleiter, S. Massberg, C. Hagl, S.P.W. Guenther, 2021. Outcome of patients treated with extracorporeal life support in cardiogenic shock complicating acute myocardial infarction: 1-year result from the ECLS-Shock study. Clinical Research in Cardiology 110(9):1412-1420.
  7. A.-L. Boulesteix, R. Groenwold, M. Abrahamowicz, H. Binder, M. Briel, R. Hornung, T. Morris, J. Rahnenführer, W. Sauerbrei, 2020. An introduction to statistical simulations in health research. BMJ Open 10:e039921.
  8. S. Klau*, S. Hoffmann*, C. Patel, J. Ioannidis, A.-L. Boulesteix, 2020. Examining the robustness of observational associations to model, measurement and sampling uncertainty with the vibration of effects framework. International Journal of Epidemiology DOI: 10.1093/ije/dyaa164. *contributed equally to this work
  9. N. Ellenbach, A.-L. Boulesteix, B. Bischl, K. Unger, R. Hornung, 2020. Improved outcome prediction across data sources through robust parameter tuning. Journal of Classification DOI:10.1007/s00357-020-09368-z.
  10. D.-M. Burgmann, K. Förster, M. Klemme, M. Delius, C. Hübener, R. Wisskott, A.-L. Boulesteix, A. Flemmer, 2020. Delivery room desaturations and bradycardia in the early postnatal period of healthy term neonates - A prospective observational study. The Journal of Maternal-Fetal & Neonatal Medicine DOI: 10.1080/14767058.2020.1757064.
  11. A.-L. Boulesteix, A. Charlton, S. Hoffmann, H. Seibold, 2020. A replication crisis in methological research? Significance 17(5):18-21.
  12. D. Samaga, R. Hornung, H. Braselmann, J. Hess, H. Zitzelsberger, C. Belka, A.-L. Boulesteix, K. Unger, 2020. Single-center versus multi-center data sets for molecular prognostic modeling: A simulation study. Radiation Oncology 15:109.
  13. R. De Bin, A.-L. Boulesteix, A. Benner, N. Becker, W. Sauerbrei, 2020. Combining clinical and molecular data in regression prediction models: insights from a simulation study. Briefings in Bioinformatics, DOI: 10.1093/bib/bbz136.
  14. M. Fuchs, R. Hornung, A.-L. Boulesteix, R. De Bin, 2020. On the asymptotic behaviour of the variance estimator of a U-statistic. Journal of Statistical Planning and Inference 209:101-111.
  15. S. Klau, M.-L. Martin-Magniette, A.-L. Boulesteix*, S. Hoffmann*, 2020. Sampling uncertainty versus method uncertainty: a general framework with applications to omics biomarker selection. Biometrical Journal 62:670-687. *contributed equally to this work.
  16. A.-L. Boulesteix, S. Hoffmann, M. Wright, I. König, 2020. Statistical learning approaches in the genetic epidemiology of complex diseases. Human Genetics. 139:73-84.
  17. A. Volkmann, R. De Bin, W. Sauerbrei, A.-L. Boulesteix, 2019. Added predictive value of omics data depends on the clinical model. BMC Medical Research Methodology 19:162.
  18. L. Weber, W. Saelens, R. Cannoodt, C. Soneson, A. Hapfelmeier, P. Gardner, A.-L. Boulesteix, Y. Saeys, M.D. Robinson, 2019. Essential guidelines for computational method benchmarking. Genome Biology 20:125.
  19. A. Guo, J. Srinath, A.-L. Boulesteix, M. Feuerecker, J. Briegel, I. Kaufmann, B. Crucian, 2019. The so-called Immune booster selenium does not step up immune function in sepsis. Journal of Critical Care 52:208-212.
  20. S. Brunner, S.P.W. Guenther, K. Lackermair, M. Orban, A.-L. Boulesteix, A. Bauer, J. Hausleiter, S. Massberg, C. Hagl, 2019. Extracorporeal life support in cardiogenic shock complicating acute myocardial infarction. Journal of the American College of Cardiology 73(18):2355-2357.
  21. P. Probst, A.-L. Boulesteix, B. Bischl, 2019. Tunability: importance of hyperparameters of machine learning algorithmus. Journal of Machine Learning Research 20(53):1-32.
  22. P. Probst, M. Wright, A.-L. Boulesteix, 2019. Hyperparameters and tuning strategies for random forest. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9(3):e1301. Previous version available at:
  23. A.-L. Boulesteix*, S. Janitza*, R. Hornung, P. Probst, H. Busen, A. Hapfelmeier, 2019. Making Complex Prediction Rules Applicable for Readers: Current Practice in Random Forest Literature and Recommendations. Biometrical Journal 61:1314-1328. Previous version available at: Technical Report 199, Department of Statistics, LMU. *contributed equally to the paper
  24. S. Janitza, E. Celik, A.-L. Boulesteix, 2018. A computationally fast variable importance test for random forests for high-dimensional data. Advances in Data Analysis and Classification 12(4):885-915.
  25. S. Klau, V. Jurinovic, R. Hornung, T. Herold, A.-L. Boulesteix, 2018. Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data. BMC Bioinformatics 19:322.
  26. R. Hornung, V. Jurinovic, A. M. N. Batcha, S. A. Bamopoulos, M. Rothenberg-Thurley, S. Amler, M. C. Sauerland, W. E. Berdel, B. J. Wörmann, S. K. Bohlander, J. Braess, W. Hiddemann, S. Lehmann, S. Mareschal, K. Spiekermann, K. H. Metzeler, T. Herold, A.-L. Boulesteix, 2018. Mediation analysis reveals common mechanisms of RUNX1 point mutations and RUNX1/RUNX1T1 fusions influencing survival of patients with acute myeloid leukemia. Scientific Reports 8:11293 (pdf)
  27. R. Couronné, P. Probst, A.-L. Boulesteix, 2018. Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinformatics 19:270.
  28. P. Probst, A.-L. Boulesteix, 2018. To tune or not to tune the number of trees in random forest? Journal of Machine Learning Research 18:6673-6690.(pdf).
  29. M. Niyazi, S. Adeberg, D. Kaul, A.-L. Boulesteix, N. Bougatf, D.F. Fleischmann, A. Grün, A. Krämer, C. Rödel, F. Eckert, F. Paulsen, K.A. Kessel, S.E. Combs, O. Oehlke, A.-L. Grosu, A. Seidlizt, A. Lattermann, M. Krause, M. Baumann, M. Guberina, M. Stuschke, V. Budach, C. Belka, J. Debus, 2018. Independent validation of a new reirradiation risk score (RRRS) for glioma patients predicting post-recurrence survival: A multicenter DKTK/ROG analysis. Radiotherapy and Oncology 127(1):121-127.
  30. E.C. Considine, G. Thomas, A.-L. Boulesteix, A.S. Khashan, L.C. Kenny, 2018. Critical review of reporting of the data analysis step in metabolomics. Metabolomics 14:7.
  31. S. Günther, M. Schirren, A.-L. Boulesteix, H. Busen, T. Poettinger, A.M. Pichlmaier, N. Khaladj, C. Hagl, 2018. Effects of the Cardio First AngelTM on chest compression performance. Technology and Health Care 26(1):69-80.
  32. H. Seibold, C. Bernau, A.-L. Boulesteix, R. De Bin, 2018. On the choice and influence of the number of boosting steps. Computational Statistics 33(3), 1195-1215.
  33. A.-L. Boulesteix, H. Binder, M. Abrahamowicz, W. Sauerbrei, 2018. On the necessity and design of studies comparing statistical methods. Biometrical Journal 60: 216-218.
  34. A.-L. Boulesteix, R. Wilson, A. Hapfelmeier, 2017. Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies. BMC Medical Research Methology 17:138.
  35. A.-L. Boulesteix, U. Strasser, 2017. The multiple facets of the multiplicity of perspectives. Discussion of "Beyond subjective and objective in statistics" by C. Hennig and A. Gelman. Journal of the Royal Statistical Society A 10.1111/rssa.12276, pp40-41.
  36. R. De Bin, A.-L. Boulesteix, W. Sauerbrei, 2017. Detection of influential points as a byproduct of resampling-based variable selection procedures. Computational Statistics & Data Analysis 116:19-31.
  37. A.-L. Boulesteix, R. De Bin, X. Jiang, M. Fuchs, 2017. IPF-LASSO: integrative L1-penalized regression with penalty factors for prediction based on multi-omics data. Computational and Mathematical Methods in Medicine doi:10.1155/2017/7691937.
  38. R. Hornung, D. Causeur, C. Bernau, A.-L. Boulesteix, 2017. Improving cross-study prediction through addon batch effect adjustment and addon normalization. Bioinformatics33:397-404.
  39. S. Wahl, A.-L. Boulesteix, A. Zierer, B. Thorand, M. van de Wiel, 2016. Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation. BMC Medical Research Methodology 16:144.
  40. U. Schönermarck, C. Dengler, A. Gmeinwieser, S. Praun, G. Schelling, M. Fischereder, A.-L. Boulesteix, M.E. Dolch, 2016. Exhaled breath volatile organic and inorganic compound composition in end-stage renal disease. Clinical Nephrology 86:132-140.
  41. A. Tufman, R.M. Huber, S. Völk, F. Aigner, M. Edelmann, F. Gamarra, R. Kiefl, K. Schrödl, F. Tian, A.-L. Boulesteix, S. Endres, S. Kobold, 2016. Interleukin-22 is elevated in lavage from patients with lung cancer and other pulmonary diseases. BMC Cancer 16:409.
  42. B. Müller, A. Wilcke, A.-L. Boulesteix, J. Brauer, E. Passarge, J. Boltze, H. Kirsten, 2016. Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives. Human Genetics 135(3):259-72.
  43. R. Hornung, A.-L. Boulesteix, D. Causeur, 2016. Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27.
  44. M.E. Dolch, S. Janitza, A.-L. Boulesteix, C. Grassmann-Lichtenauer, S. Praun, W. Denzer, G. Schelling, S. Schubert, 2016. Gram-negative and -positive bacteria differentiation in blood culture samples by headspace volatile compound analysis. Journal of Biological Research - Thessaloniki 23:3.
  45. G. Casalicchio, B. Bischl, A.-L. Boulesteix, M. Schmid, 2016. The residual-based predictiveness curve – A visual tool to assess the performance of prediction models. Biometrics 72:392-401.
  46. S. Janitza, G. Tutz, A.-L. Boulesteix, 2016. Random forest for ordinal response data: prediction and variable selection. Computational Statistics & Data Analysis 96:57-73.
  47. S. Rospleszcz*, S. Janitza*, A.-L. Boulesteix, 2016. Categorical variables with many categories are preferentially selected in model selection procedures for multivariable regression models on bootstrap samples. Biometrical Journal 58:652-673. *both authors contributed equally to the paper.
  48. S. Janitza, H. Binder, A.-L. Boulesteix, 2016. Pitfalls of hypothesis tests and model selection on bootstrap samples: causes and consequences in biometrical applications. Biometrical Journal 58:447-473.
  49. R. De Bin, S. Janitza, W. Sauerbrei, A.-L. Boulesteix, 2016. Subsampling versus bootstrapping in resampling-based model selection for multivariable regression. Biometrics 72:272-280.
  50. R. Hornung, C. Bernau, C. Truntzer, T. Stadler, A.-L. Boulesteix, 2015. Full versus incomplete cross-validation: measuring the impact of imperfect separation between training and test sets in prediction error estimation. BMC Medical Research Methodology 15:95.
  51. A.-L. Boulesteix, V. Stierle, A. Hapfelmeier, 2015. Publication bias in methodological computational research. Cancer Informatics Suppl. 5:11-19.
  52. A.-L. Boulesteix, R. Hable, S. Lauer, M. Eugster, 2015. A statistical framework for hypothesis testing in real data comparison studies. The American Statistician 69:201-212. Previous version available at: Technical Report 136, Department of Statistics, LMU.
  53. W. Sauerbrei, A. Buchholz, A.-L. Boulesteix, H. Binder, 2015. On stability issues in deriving multivariable regression models. Biometrical Journal 57:531-555.
  54. A.-L. Boulesteix, 2015. Ten simple rules for reducing overoptimistic reporting in methodological computational research. PLoS Computational Biology 11(4): e1004191.
  55. B.M. Böll, F. Vogt, A.-L. Boulesteix, C. Schmitz, 2015. Gender mismatch in allograft aortic valve surgery. Interactive Cardiovascular and Thoracic Surgery pii: ivv151 (Epub ahead of print)
  56. D. Schwilling, M. Vogeser, F. Kirchhoff, F. Schwaiblmair, A.-L. Boulesteix, A. Schulze, A.W. Flemmer, 2015. Live music reduces stress levels in very low-birthweight infants. Acta Paediatrica 104:360-367.
  57. A.-L. Boulesteix, 2015. On reviews and papers on new methods (letter to the editors). Briefings in Bioinformatics 16:365-366.
  58. A.-L. Boulesteix, S. Janitza, A. Hapfelmeier, K. van Steen, C. Strobl, 2015. On the term "interaction" and related phrases in the literature on random forests. Briefings in Bioinformatics 16:338-345. (open-access pdf).
  59. R. De Bin, T. Herold, A.-L. Boulesteix, 2014. Added predictive value of omics data: specific issues related to validation illustrated by two case studies. BMC Medical Research Methodology 14:117.
  60. R. De Bin, W. Sauerbrei, A.-L. Boulesteix, 2014. Investigating the prediction ability of survival models based on both clinical and omics data: two case studies. Statistics in Medicine 33:5310-5329. Technical Report 153, Department of Statistics, LMU.
  61. A.-L. Boulesteix and Matthias Schmid, 2014. Discussion: Machine learning versus statistical modelling. Biometrical Journal 56:588-593.
  62. C. Bernau, M. Riester, A.-L. Boulesteix , G. Parmigiani, C. Huttenhower, L. Waldron, L. Trippa, 2014. Cross-study validation for the assessment of prediction algorithms. Bioinformatics 30: i105-i112.
  63. M. Kebschull, P. Guarnieri, R.T. Demmer, A.-L. Boulesteix, P. Pavlidis, P.N. Papapanou, 2013. Molecular differences between chronic and aggressive periodontitis. Journal of Dental Research 92:1081-1088.
  64. A.-L. Boulesteix, 2013. On representative and illustrative comparisons with real data in bioinformatics: comment on the letter to the editor by Smith et al. Bioinformatics 29:2664-6.
  65. C. Bernau, T. Augustin, A.-L. Boulesteix, 2013. Correcting the optimally selected resampling-based error rate: A smooth analytical alternative to nested cross-validation. pdf of an older version (technical report). Biometrics 69:693-702. Companion Website.
  66. S. Pildner von Steinburg, A.-L .Boulesteix, C. Lederer, S. Grunow, S. Schiermeier, W. Hatzmann, K.-T. M. Schneider, M. Daumer, 2013. What is the “normal” fetal heart rate? PeerJ 1:e82.
  67. A.-L. Boulesteix*, S. Lauer*, M. Eugster, 2013. A plea for neutral comparison studies in computational sciences. PLoS One 8(4):e61562. * both authors contributed equally to this work. Companion Website.
  68. V. Guillemot, A. Bender, A.-L. Boulesteix, 2013. Iterative reconstruction of high-dimensional Gaussian graphical models based on a new method to estimate partial correlations under constraints. PLoS One 8(4): e60536. Companion Website.
  69. S. Janitza, C. Strobl, A.-L. Bouleteix, 2013. An AUC-based Permutation Variable Importance Measure for Random Forests. BMC Bioinformatics 14:119.  highlyaccessed
  70. F. Vogt, B.M. Böll, A.-L. Boulesteix, E. Kilian, G. Santarpino, B. Reichart, C. Schmitz, 2013. Homografts in aortic position: does blood group incompatibility have an impact on patient outcomes? Interactive Cardiovascular and Thoracic Surgery16:619-24.
  71. C. Hornuss, A. Zagler, M.E. Dolch, D. Wiepcke, S. Praun, A.-L. Boulesteix, F. Weis, C. C. Apfel, G. Schelling, 2012. Breath isoprene concentrations in persons undergoing general anesthesia and in healthy volunteers. Journal of Breath Research 6(4):046004.
  72. A.-L. Boulesteix, S. Janitza, J. Kruppa, I. König, 2012. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. pdf (technical report). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2:497-503.
  73. C. Bernau, A.-L. Boulesteix, J. Knaus, 2012. Application of microarray analysis on computer cluster and cloud platforms. Methods of Information in Medicine 52:65-71.
  74. A.-L. Boulesteix, A. Bender, J. Lorenzo Bermejo, C. Strobl, 2012. Random forest Gini importance favors SNPs with large minor allele frequency. Technical Report 106, Department of Statistics, LMU. Briefings in Bioinformatics 13: 292-304. Companion Website.
  75. W. Sauerbrei, A.-L. Boulesteix, H. Binder, 2011. Stability investigations of multivariable regression models derived from low and high dimensional data. Journal of Biopharmaceutical Statistics 21:1206-1231.
  76. A.C. Pickhard, J. Margraf, A. Knopf, T. Stark, G. Piontek, C. Beck, A.-L. Boulesteix, E.Q. Scherer, S. Pigorsch, J. Schlegel, W. Arnold, R. Reiter, 2011. Inhibition of Radiation Induced Migration of Human Head and Neck Squamous Cell Carcinoma by Blocking EGF Receptor Pathways. BMC Cancer 11:388.
  77. M. Calle, V. Urrea, A.-L. Boulesteix, N. Malats, 2011. AUC-RF: A new strategy for genomic profiling with Random Forest. Human Heredity 72:121-132.
  78. T. Herold, V. Jurinovic, K.H. Metzeler, A.-L. Boulesteix, M. Bergmann, T. Seiler, M. Mulaw, S. Thoene, A. Dufour, Z. Pasalic, M. Schmidberger, M. Schmidt, S. Schneider, P.M. Kakadia, M. Feuring-Buske, J. Braess, K. Spiekermann, U. Mansmann, W. Hiddemann, C. Buske, S.K. Bohlander. An eight-gene expression signature for the prediction of survival and time to treatment in chronic lymphocytic leukemia. Leukemia 25:1639-1645.
  79. A.-L. Boulesteix, V. Guillemot, W. Sauerbrei, 2011. Use of pre-transformation to cope with extreme values in important candidate features. Biometrical Journal 53:673–688. Companion Website.
  80. A.-L. Boulesteix, 2011. Editorial. Briefings in Bioinformatics 12:187-188.
  81. A.-L. Boulesteix, W. Sauerbrei, 2011. Added predictive value of high-throughput molecular data to clinical data, and its validation. Briefings in Bioinformatics 12:215-229.
  82. M. Jelizarow, V. Guillemot, A. Tenenhaus, K. Strimmer, A.-L. Boulesteix, 2010. Over-optimism in bioinformatics: an illustration. pdf. Bioinformatics 26:1990-1998. Companion Website.
  83. A.-L. Boulesteix, T. Hothorn, 2010. Testing the additional predictive value of high-dimensional data. BMC Bioinformatics 11:78. highlyaccessed
  84. S. Eifert, H. Mair, A.-L. Boulesteix, E. Kilian, M. Adamczak, B. Reichart, P. Lamm, 2010. Mid term outcomes of patients with PCI prior to CABG in comparison to patients with primary CABG. Vascular Health Risk Management 6:495-501.
  85. A.-L. Boulesteix, 2010. Over-optimism in bioinformatics research. pdf. Bioinformatics 26:437-439.
  86. D. Doll, L. Keller, M. Maak, A.-L. Boulesteix, J. Siewert, B. Holzmann, K.-P. Jansen, 2010. Differential expression of the chemokines GRO-2, GRO-3 and Interleukin-8 in colon cancer and their impact on metastatic disease and survival. International Journal of Colorectal Disease 25:573-581.
  87. A.-L. Boulesteix, C. Strobl, 2009. Optimal classifier selection and negative bias in error rate estimation: An empirical study on high-dimensional prediction. BMC Medical Research Methodology 9:85. Companion Website.
  88. N. Krämer, J. Schäfer, A.-L. Boulesteix, 2009. Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics 10:384.
  89. A.-L. Boulesteix, M. Slawski, 2009. Stability and aggregation of ranked gene lists. pdf. Briefings in Bioinformatics 10:556-568.
  90. W. van Wieringen, D. Kun, R. Hampel, A.-L. Boulesteix, 2009. Survival prediction using gene expression data: a review and comparison. pdf. pdf. Computational Statistics and Data Analysis  53:1590-1603.
  91. M. Slawski, M. Daumer, A.-L. Boulesteix, 2008. CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data. pdf. BMC Bioinformatics 9: 439. highlyaccessed
  92. A.-L. Boulesteix, C. Porzelius, M. Daumer, 2008. Microarray-based classification and clinical predictors: On combined classifiers and additional predictive value. pdf. reprint. Bioinformatics 24:1698-1706.
  93. C. Strobl, A.-L. Boulesteix, T. Kneib, T. Augustin, A. Zeileis, 2008. Conditional variable importance for random forests. pdf. BMC Bioinformatics 9:307. highlyaccessed
  94. N. Krämer, A.-L. Boulesteix and G. Tutz, 2008. Penalized partial least squares with applications to B-splines and functional data. pdf. Chemometrics and Intelligent Laboratory Systems 94:60-69.(accepted).
  95. R. Reiter, P. Gais, M.K. Steuer-Vogt, A.-L. Boulesteix, T. Deutschle, R. Hampel, S. Wagenpfeil, S. Rausen, A. Walch, K. Bink, U. Jutting, F. Neff, W. Arnold, H. Hofler, A. Pickhard, 2009. Centrosome abnormalities in head and neck squamous cell carcinoma (HNSCC). Acta Otolaryngologica 129:205-213.
  96. A.-L. Boulesteix, A. Kondylis, N. Krämer, 2008. Invited comment on: "Augmenting the bootstrap to analyze high dimensional genomic data" by Tyekucheva and Chiaromonte. pdf. TEST 17:31-35.
  97. T. Wichard, S. Poulet, A.-L. Boulesteix, J.-B. Ledoux, B. Lebreton, J. Marchetti, G. Pohnert, 2008. Influence of diatoms on copepod reproduction. II. Uncorrelated effects of diatom-derived alpha, beta, gamma, delta-unsaturated aldehydes and polyunsaturated fatty acids on Calanus helgolandicus in the field. Progress in Oceanography 77:30-44 .
  98. A.-L. Boulesteix, C. Strobl, T. Augustin, M. Daumer, 2008. Evaluating microarray-based classifiers: an overview. pdf. Cancer Informatics 6:77-97.
  99. C. Rimkus, J. Friederichs, A.-L. Boulesteix, J. Mages, K. Becker, H. Nekarda, R. Rosenberg, K.P. Janssen, J.R. Siewert, 2008. Microarray-based prediction of tumor response to neoadjuvant radiochemotherapy of patients with locally advanced rectal cancer. Clinical Gastroenterology and Hepatology 6:53-61.
  100. D. Doll, J. Friederichs, A.-L. Boulesteix, W. Düsel, F. Fend, S. Petersen, 2008. Surgery for asymptomatic pilonidal sinus disease. International Journal of Colorectal Disease 23:839-844.
  101. D. Doll, J. Friederichs, H. Dettmann, A.-L. Boulesteix, W. Düsel, S. Petersen, 2008. Time and rate of sinus formation in pilonidal sinus disease. International Journal of Colorectal Disease 23:359-364.
  102. D. Doll, A. Novotny, R. Rothe, K. Wietelmann, A.-L. Boulesteix, W. Düsel, S. Petersen, 2008. Methylene Blue halves the long term recurrence rate in acute pilonidal sinus disease. International Journal of Colorectal Disease 23:181-187.
  103. W. Dietrich, R. Busley, A.-L. Boulesteix, 2008. Effects of aprotinin dosage on renal function: an analysis of 8,548 cardiac surgical patients treated with different dosages of aprotinin. Anesthesiology108:189-198.
  104. A.-L. Boulesteix, C. Strobl, S. Weidinger, H.E. Wichmann, S. Wagenpfeil, 2007. Multiple testing for SNP-SNP interactions. pdf. Statistical Applications in Genetics and Molecular Biology 6(1):37.
  105. A.-L. Boulesteix and C. Strobl, 2007. Maximally selected chi-square statistics and non-monotonic associations: an exact approach based on two cutpoints. pdf (preprint), Computational Statistics and Data Analysis 51(12):6295-6306.
  106. C. Strobl, A.-L. Boulesteix and T. Augustin, 2007. Unbiased split selection for classification trees based on the Gini index. pdf. Computational Statistics and Data Analysis 52:483-501.
  107. A.-L. Boulesteix and K. Strimmer, 2007. Partial Least Squares: A versatile tool for the analysis of high-dimensional genomic data. pdf. Briefings in Bioinformatics 8(1):32-44.
  108. A.-L. Boulesteix, 2007. WilcoxCV: An R package for fast variable selection in cross-validation. pdf (preprint), Bioinformatics 23: 1702-1704.
  109. W. Dietrich, A. Ebell, R. Busley, A.-L. Boulesteix, 2007. Aprotinin and Anaphylaxis - Analysis of 12403 Exposures to Aprotinin in Cardiac Surgery. The Annals of Thoracic Surgery 84:1144-1150.
  110. R. Napieralski, K. Ott, M. Kremer, K. Becker, A.-L. Boulesteix, F. Lordick, J.R. Siewert, H. Höfler, G. Keller, 2007. Methylation of Tumor-Related Genes in Neoadjuvant-Treated Gastric Cancer: Relation to Therapy Response and Clinicopathologic and Molecular Features. Clinical Cancer Research 13(17):5095-5102.
  111. C. Strobl, A.-L. Boulesteix, A. Zeileis and T. Hothorn, 2007. Bias in random forest variable importance measures: Illustrations, sources and a solution. pdf. BMC Bioinformatics 8:25. highlyaccessed
  112. A.-L. Boulesteix, 2006. Reader's reaction to 'Dimension reduction for classification with gene expression microarray data' by Dai et al (2006), Statistical Applications in Genetics and Molecular Biology 5(1), Article 16, pdf.
  113. A.L. Boulesteix, 2006. Maximally selected chi-square statistics and binary splits of nominal variables, pdf, Biometrical Journal 48:838-848.
  114. A.-L. Boulesteix, 2006. Maximimally selected chi-square statistics for ordinal variables, pdf, Biometrical Journal 48:451-462.
  115. A.-L. Boulesteix and G. Tutz, 2006. Identification of Interaction Patterns and Classification with Applications to Microarray Data, pdf (preprint), Computational Statistics and Data Analysis 50: 783-802.
  116. A.-L. Boulesteix and K. Strimmer, 2005. Predicting Transcription Factor Activities from Combined Analysis of Microarray and ChIP Data: A Partial Least Squares Approach, Theoretical Biology and Medical Modelling 2:23, pdf.
  117. S. Hiendleder, S. Bauersachs, A.-L. Boulesteix, H. Blum, G. Arnold, T. Fröhlich and E. Wolf, 2005. Functional genomics: tools for improving farm animal health and welfare, Rev Sci Tech. 24(1): 354-377, "Biotechnology and animal health", pdf.
  118. A.-L. Boulesteix, 2004. PLS Dimension Reduction for Classification with Microarray Data. pdf (preprint). Statistical Applications in Genetics and Molecular Biology 3(1), Article 33.
  119. A.-L. Boulesteix, G. Tutz and K. Strimmer, 2003. A CART-based approach to discover emerging patterns in microarray data. Bioinformatics 19: 2465-2472, pdf.

Other publications including proceedings articles (12)

  1. A.-L. Boulesteix, T. Morris, W. Sauerbrei, M. Abrahamowicz, on behalf of the STRATOS Simulation Panel, 2020. Introducing the Simulation Panel (SP). Biometric Bulletin 37(2):11-12.
  2. A.-L. Boulesteix, M. Hatz, 2017. Benchmarking for clustering methods based on real data: a statistical view. In “Data Science - Innovative Developments in Data Analysis and Clustering”, pp 73-82. Publisher: Springer.
  3. A.-L. Boulesteix, 2016. Which resampling-based error estimator for benchmark studies? A power analysis with application to PLS-LDA. "The Multiple Facets of Partial Least Squares Methods". Editors: Abdi, H., Esposito Vinzi, V., Russolillo, G., Saporta, G., Trinchera, L. (Eds). Publisher : Springer. Companion Website.
  4. A.-L. Boulesteix, A. Richter, C. Bernau, 2013. Complexity selection with cross-validation for lasso and sparse partial least squares using high-dimensional data. In: Algorithms from and for Nature and Life. Springer, pp. 261–268. Companion Website.
  5. M. Oelker, A.-L. Boulesteix, 2013. On the simultaneous analysis of clinical and omics data - a comparison of globalboosttest and pre-validation techniques. pdf (technical report). Proceedings of the 8th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, 259-268. Eds.: Giudici, P., Ingrassia, S., Vichi, M. ISBN 978-3-319-00031-2. Companion Website.
  6. V. Guillemot, M. Jelizarow, A. Tenenhaus, A.-L. Boulesteix, 2011. SHrinkage covariance estimation incorporating prior biological knowledge with applications to high-dimensional data. Technical Report 107, Department of Statistics, LMU. Proceedings of the 58th ISI World Statistics Congress. Companion Website.
  7. C. Bernau, A.-L. Boulesteix, 2010. Variable selection and parameter tuning in high-dimensional prediction. Technical Report 76, Department of Statistics, LMU. COMPSTAT 2010.
  8. S. Pildner von Steinburg, D. Chronas, A.-L. Boulesteix, N. Lack, K.T.M. Schneider, 2007. Risikofaktoren für Frühgeburtlichkeit - eine multivariate Analyse der bayrischen Perinataldaten aus 10 Jahren. Zeitschrift für Geburtshilfe und Neonatologie:211.
  9. M. Daumer, M. Scholz, A.-L. Boulesteix, S. Pildner von Steinburg, S. Schiermeier, W. Hatzmann, K.T.M. Schneider, 2007. The normal fetal heart rate study: Analysis plan. Nature Precedings, doi:10.1038/npre.2007.980.1.
  10. N. Henningsen, K. Ott, K. Becker, A.-L. Boulesteix, F. Lordick, J.R. Siewert, H. Hofler, G. Keller, 2007. Polymorphisms in the nucleotide excision repair genes ERCC1 and ERCC2 and association with response and prognosis in neoadjuvant treated gastric cancer patients. Pathology Research and Practice 203:254-259.
  11. A.-L. Boulesteix, V. Hösel and V. Liebscher, 2007. Stochastic Modeling for the COMET-assay. Journal of Concrete and Applicable Mathematics 5(1):53:75.
  12. A.-L. Boulesteix, 2005. A note on between-group PCA, International Journal of Pure and Applied Mathematics 19: 359-366.


  1. A.-L. Boulesteix, M. Fuchs, ipflasso R package. Integrative Lasso with penalty factors.
  2. A.-L. Boulesteix, T. Hothorn, globalboosttest R package. Testing the additional predictive value of high-dimensional data.
  3. A.-L. Boulesteix, MAclinical R package. Class prediction based on microarray data and clinical parameters.
  4. M. Slawski and A.-L. Boulesteix, The GeneSelector Bioconductor package. The term 'GeneSelector' refers to a filter selecting those genes which are consistently identified as differentially expressed using various statistical procedures. 'Selected' genes are those present at the top of the list in various featured ranking methods (currently 15). In addition, the stability of the findings can be taken in account in the final ranking by examining perturbed versions of the original data set, e.g. by leaving samples, swapping class labels, generating bootstrap replicates or adding noise.
  5. M. Slawski, C. Bernau and A.-L. Boulesteix, The CMA Bioconductor package. This package provides a comprehensive collection of various microarray-based classification algorithms both from machine learning and statistics. Variable selection, hyperparameter tuning, evaluation and comparison can be performed in a combined manner or stepwise in a user-friendly environment.
  6. N. Kraemer and A.-L. Boulesteix, ppls R package. Penalized partial least squares.
  7. A.-L. Boulesteix, WilcoxCV R package. Wilcoxon-based variable selection in cross-validation.
  8. A.-L. Boulesteix, SNPmaxsel R package. Maximally selected statistics for SNP data.
  9. A.-L. Boulesteix, exactmaxsel R package. Maximally selected statistics for binary response variables - Exact methods.
  10. A.-L. Boulesteix, Sophie Lambert-Lacroix, Julie Peyre and Korbinian Strimmer, plsgenomics R package. PLS analyses for genomics.


A.-L. Boulesteix, 2011. Habilitation, Université Evry-Val d'Essonne, France.

A.-L. Boulesteix, 2005. phD Thesis: Dimension Reduction and Classification with high-dimensional microarray data, pdf.

A.-L. Boulesteix, 2001. Mathematische Modellierung und Statistik für das COMET-assay (Master thesis at the University of Stuttgart, Department of Mathematics A)


  • "Introduction to Medical Biometry" (winter 2016-2017), lecture and tutorials for statistics students (bachelor), joint with Sonja Greven and Almond Ströcker.
  • Medical Statistics (winter 2016-2017), seminar for statistics students (bachelor).
  • "Statistical methods in genomics and proteomics" (winter 2016-2017), lecture for statistics/biostatistics students (master)
  • Biostatistical Methods (summer 2016), lecture for statistics/biostatistics students (master).
  • Applied Statistics in Epidemiology (summer 2016), joint seminar for statistics and epidemiology students (master).
  • Statistical methods in genomics and proteomics (winter 2015-2016), lecture for statistics/biostatistics students (master).
  • Biostatistical Methods (summer 2015), lecture for statistics/biostatistics students (master).
  • Statistical planning and analysis of randomized controlled clinical trials (winter 2014-2015), seminar for statistics students (bachelor).
  • Longitudinal data analysis (summer 2014), lecture for statistics/biostatistics students (master).
  • Statistical methods in genomics and proteomics (winter 2013-2014), lecture for statistics/biostatistics students (master).
  • Medical Statistics (winter 2013-2014), seminar for statistics students (bachelor).
  • Longitudinal data analysis (summer 2013), lecture for statistics/biostatistics students (master).
  • Statistical methods in genomics and proteomics (winter 2012-2013), lecture for statistics/biostatistics students (master).
  • Medical Statistics (winter 2012-2013), seminar for statistics students (bachelor).
  • Longitudinal data analysis (summer 2012), lecture for statistics/biostatistics students (master).
  • Reproducibility (winter 2011-2012), seminar for statistics/biostatistics students (master).
  • Statistical methods in genomics and proteomics (winter 2011-2012), lecture for statistics/biostatistics students (master).
  • Longitudinal data analysis (summer 2011), lecture for statistics/biostatistics students (master).
  • Validation in medical statistics (winter 2010-2011), seminar for statistics/biostatistics students (bachelor, master).
  • Longitudinal data analysis (summer 2010), lecture for statistics/biostatistics students (master)
  • Statistical methods in genomics and proteomics (winter 2009-2010), lecture for statistics/biostatistics students (master)
  • Longitudinal data analysis (summer 2010), lecture for statistics/biostatistics students (master)
  • Advanced Quantitative Methods (summer 2009), course on GGMs (MPH)
  • Statistical methods in genomics and proteomics (winter 2009-2010), lecture for statistics/biostatistics students (master)
  • Longitudinal data and time series (summer 2008), lecture for statistics/biostatistics students (master)
  • Medical statistics (february 2008), lecture for mathematics students (master) at the Ecole Centrale Paris (France)
  • Biostatistics (February 2007), lecture for mathematics students (master) at the Ecole Centrale Paris (France)


    I have (co-)supervised 35 master or diploma theses and 30 bachelor theses since 2004.