A CART-based approach to discover emerging patterns in microarray data. - Anne-Laure Boulesteix; Gerhard Tutz 001; Korbinian Strimmer (Bioinform., 2003)

Identification of interaction patterns and classification with applications to microarray data. - Anne-Laure Boulesteix; Gerhard Tutz 001 (Comput. Stat. Data Anal., 2006)

Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. - Anne-Laure Boulesteix; Korbinian Strimmer (Briefings Bioinform., 2007)

WilcoxCV: an R package for fast variable selection in cross-validation. - Anne-Laure Boulesteix (Bioinform., 2007)

Bias in random forest variable importance measures: Illustrations, sources and a solution. - Carolin Strobl; Anne-Laure Boulesteix; Achim Zeileis; Torsten Hothorn (BMC Bioinform., 2007)

Maximally selected Chi-squared statistics and non-monotonic associations: An exact approach based on two cutpoints. - Anne-Laure Boulesteix; Carolin Strobl (Comput. Stat. Data Anal., 2007)

Unbiased split selection for classification trees based on the Gini Index. - Carolin Strobl; Anne-Laure Boulesteix; Thomas Augustin 001 (Comput. Stat. Data Anal., 2007)

Microarray-based classification and clinical predictors: on combined classifiers and additional predictive value. - Anne-Laure Boulesteix; Christine Porzelius; Martin Daumer (Bioinform., 2008)

CMA - a comprehensive Bioconductor package for supervised classification with high dimensional data. - Martin Slawski; Martin Daumer; Anne-Laure Boulesteix (BMC Bioinform., 2008)

Conditional variable importance for random forests. - Carolin Strobl; Anne-Laure Boulesteix; Thomas Kneib; Thomas Augustin 001; Achim Zeileis (BMC Bioinform., 2008)

Stability and aggregation of ranked gene lists. - Anne-Laure Boulesteix; Martin Slawski (Briefings Bioinform., 2009)

Regularized estimation of large-scale gene association networks using graphical Gaussian models. - Nicole Krämer 002; Juliane Schäfer; Anne-Laure Boulesteix (BMC Bioinform., 2009)

Survival prediction using gene expression data: A review and comparison. - Wessel N. van Wieringen; David Kun; Regina Hampel; Anne-Laure Boulesteix (Comput. Stat. Data Anal., 2009)

Over-optimism in bioinformatics research. - Anne-Laure Boulesteix (Bioinform., 2010)

Over-optimism in bioinformatics: an illustration. - Monika Jelizarow; Vincent Guillemot; Arthur Tenenhaus; Korbinian Strimmer; Anne-Laure Boulesteix (Bioinform., 2010)

Testing the additional predictive value of high-dimensional molecular data. - Anne-Laure Boulesteix; Torsten Hothorn (BMC Bioinform., 2010)

Editorial. - Anne-Laure Boulesteix (Briefings Bioinform., 2011)

Added predictive value of high-throughput molecular data to clinical data and its validation. - Anne-Laure Boulesteix; Willi Sauerbrei (Briefings Bioinform., 2011)

Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations. - Anne-Laure Boulesteix; Andreas Bender 001; Justo Lorenzo Bermejo; Carolin Strobl (Briefings Bioinform., 2012)

A Plea for Neutral Comparison Studies in Computational Sciences - Anne-Laure Boulesteix; Manuel J. A. Eugster (CoRR, 2012)

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. - Anne-Laure Boulesteix; Silke Janitza; Jochen Kruppa; Inke R. König (WIREs Data Mining Knowl. Discov., 2012)

On representative and illustrative comparisons with real data in bioinformatics: response to the letter to the editor by Smith et al.. - Anne-Laure Boulesteix (Bioinform., 2013)

An AUC-based permutation variable importance measure for random forests. - Silke Janitza; Carolin Strobl; Anne-Laure Boulesteix (BMC Bioinform., 2013)

Complexity Selection with Cross-validation for Lasso and Sparse Partial Least Squares Using High-Dimensional Data. - Anne-Laure Boulesteix; Adrian Richter; Christoph Bernau (Algorithms from and for Nature and Life, 2013)

On the Simultaneous Analysis of Clinical and Omics Data: A Comparison of Globalboosttest and Pre-validation Techniques. - Margret-Ruth Oelker; Anne-Laure Boulesteix (Statistical Models for Data Analysis, 2013)

Cross-study validation for the assessment of prediction algorithms. - Christoph Bernau; Markus Riester; Anne-Laure Boulesteix; Giovanni Parmigiani; Curtis Huttenhower; Levi Waldron; Lorenzo Trippa (Bioinform., 2014)

Letter to the Editor: On Reviews and Papers on New Methods. - Anne-Laure Boulesteix (Briefings Bioinform., 2015)

Letter to the Editor: On the term 'interaction' and related phrases in the literature on Random Forests. - Anne-Laure Boulesteix; Silke Janitza; Alexander Hapfelmeier; Kristel Van Steen; Carolin Strobl (Briefings Bioinform., 2015)

Ten Simple Rules for Reducing Overoptimistic Reporting in Methodological Computational Research. - Anne-Laure Boulesteix (PLoS Comput. Biol., 2015)

Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. - Roman Hornung; Anne-Laure Boulesteix; David Causeur (BMC Bioinform., 2016)

Random forest for ordinal responses: Prediction and variable selection. - Silke Janitza; Gerhard Tutz 001; Anne-Laure Boulesteix (Comput. Stat. Data Anal., 2016)

Improving cross-study prediction through addon batch effect adjustment or addon normalization. - Roman Hornung; David Causeur; Christoph Bernau; Anne-Laure Boulesteix (Bioinform., 2017)

IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data. - Anne-Laure Boulesteix; Riccardo De Bin; Xiaoyu Jiang; Mathias Fuchs (Comput. Math. Methods Medicine, 2017)

Detection of influential points as a byproduct of resampling-based variable selection procedures. - Riccardo De Bin; Anne-Laure Boulesteix; Willi Sauerbrei (Comput. Stat. Data Anal., 2017)

To Tune or Not to Tune the Number of Trees in Random Forest. - Philipp Probst; Anne-Laure Boulesteix (J. Mach. Learn. Res., 2017)

A computationally fast variable importance test for random forests for high-dimensional data. - Silke Janitza; Ender Celik; Anne-Laure Boulesteix (Adv. Data Anal. Classif., 2018)

Random forest versus logistic regression: a large-scale benchmark experiment. - Raphaël Couronné; Philipp Probst; Anne-Laure Boulesteix (BMC Bioinform., 2018)

Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data. - Simon Klau; Vindi Jurinovic; Roman Hornung; Tobias Herold; Anne-Laure Boulesteix (BMC Bioinform., 2018)

On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models. - Heidi Seibold; Christoph Bernau; Anne-Laure Boulesteix; Riccardo De Bin (Comput. Stat., 2018)

Tunability: Importance of Hyperparameters of Machine Learning Algorithms. - Philipp Probst; Anne-Laure Boulesteix; Bernd Bischl (J. Mach. Learn. Res., 2019)

Hyperparameters and tuning strategies for random forest. - Philipp Probst; Marvin N. Wright; Anne-Laure Boulesteix (WIREs Data Mining Knowl. Discov., 2019)

Combining clinical and molecular data in regression prediction models: insights from a simulation study. - Riccardo De Bin; Anne-Laure Boulesteix; Axel Benner; Natalia Becker; Willi Sauerbrei (Briefings Bioinform., 2020)

Large-scale benchmark study of survival prediction methods using multi-omics data. - Moritz Herrmann; Philipp Probst; Roman Hornung; Vindi Jurinovic; Anne-Laure Boulesteix (Briefings Bioinform., 2021)

NetCoMi: network construction and comparison for microbiome data in R. - Stefanie Peschel; Christian L. Müller; Erika von Mutius; Anne-Laure Boulesteix; Martin Depner (Briefings Bioinform., 2021)

Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning. - Nicole Ellenbach; Anne-Laure Boulesteix; Bernd Bischl; Kristian Unger; Roman Hornung (J. Classif., 2021)

Interaction forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects. - Roman Hornung; Anne-Laure Boulesteix (Comput. Stat. Data Anal., 2022)

Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results. - Christina Nießl; Moritz Herrmann; Chiara Wiedemann; Giuseppe Casalicchio; Anne-Laure Boulesteix (WIREs Data Mining Knowl. Discov., 2022)

Validation of cluster analysis results on validation data: A systematic framework. - Theresa Ullmann; Christian Hennig; Anne-Laure Boulesteix (WIREs Data Mining Knowl. Discov., 2022)

Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study. - Roman Hornung; Frederik Ludwigs; Jonas Hagenberg; Anne-Laure Boulesteix (CoRR, 2023)

Evaluating machine learning models in non-standard settings: An overview and new findings. - Roman Hornung; Malte Nalenz; Lennart Schneider; Andreas Bender 001; Ludwig Bothmann; Bernd Bischl; Thomas Augustin 001; Anne-Laure Boulesteix (CoRR, 2023)

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. - Bernd Bischl; Martin Binder; Michel Lang; Tobias Pielok; Jakob Richter; Stefan Coors; Janek Thomas; Theresa Ullmann; Marc Becker; Anne-Laure Boulesteix; Difan Deng; Marius Lindauer (WIREs Data. Mining. Knowl. Discov., 2023)

Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering. - Theresa Ullmann; Stefanie Peschel; Philipp F. M. Baumann; Christian L. Müller; Anne-Laure Boulesteix (PLoS Comput. Biol., 2023)

Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study. - Theresa Ullmann; Anna Beer 001; Maximilian Hünemörder; Thomas Seidl 001; Anne-Laure Boulesteix (Adv. Data Anal. Classif., 2023)

A white paper on good research practices in benchmarking: The case of cluster analysis. - Iven Van Mechelen; Anne-Laure Boulesteix; Rainer Dangl; Nema Dean; Christian Hennig; Friedrich Leisch; Douglas L. Steinley; Matthijs J. Warrens (WIREs Data. Mining. Knowl. Discov., 2023)

Position: Why We Must Rethink Empirical Research in Machine Learning. - Moritz Herrmann; F. Julian D. Lange; Katharina Eggensperger; Giuseppe Casalicchio; Marcel Wever; Matthias Feurer; David Rügamer; Eyke Hüllermeier; Anne-Laure Boulesteix; Bernd Bischl (ICML, 2024)

Understanding random forests and overfitting: a visualization and simulation study. - Lasai Barreñada; Paula Dhiman; Dirk Timmerman; Anne-Laure Boulesteix; Ben Van Calster (CoRR, 2024)

Constructing Confidence Intervals for 'the' Generalization Error - a Comprehensive Benchmark Study. - Hannah Schulz-Kümpel; Sebastian Fischer; Thomas Nagler; Anne-Laure Boulesteix; Bernd Bischl; Roman Hornung (CoRR, 2024)

Beyond algorithm hyperparameters: on preprocessing hyperparameters and associated pitfalls in machine learning applications. - Christina Sauer; Anne-Laure Boulesteix; Luzia Hanßum; Farina Hodiamont; Claudia Bausewein; Theresa Ullmann (CoRR, 2024)

Raising awareness of uncertain choices in empirical data analysis: A teaching concept toward replicable research practices. - Maximilian M. Mandl; Sabine Hoffmann; Sebastian Bieringer; Anna E. Jacob; Marie Kraft; Simon Lemster; Anne-Laure Boulesteix (PLoS Comput. Biol., 2024)