Anne-Laure Boulesteix

发表

Maximally selected Chi-squared statistics and non-monotonic associations: An exact approach based on two cutpoints

Carolin Strobl, Anne-Laure Boulesteix, A. Boulesteix, 2007, Comput. Stat. Data Anal..

Letter to the Editor: On the term ‘interaction’ and related phrases in the literature on Random Forests

Carolin Strobl, Anne-Laure Boulesteix, Silke Janitza, 2014, Briefings Bioinform..

A CART-based approach to discover emerging patterns in microarray data

Gerhard Tutz, Anne-Laure Boulesteix, Korbinian Strimmer, 2003, Bioinform..

Correcting the Optimal Resampling‐Based Error Rate by Estimating the Error Rate of Wrapper Algorithms

Anne-Laure Boulesteix, Thomas Augustin, Christoph Bernau, 2013, Biometrics.

Survival prediction using gene expression data: A review and comparison

Anne-Laure Boulesteix, Wessel N. van Wieringen, David Kun, 2009, Comput. Stat. Data Anal..

Letter to the Editor: On Reviews and Papers on New Methods

Anne-Laure Boulesteix, 2015, Briefings Bioinform..

Erratum to: Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation

Anne-Laure Boulesteix, Simone Wahl, Mark A. van de Wiel, 2016, BMC Medical Research Methodology.

The residual‐based predictiveness curve: A visual tool to assess the performance of prediction models

Bernd Bischl, Anne-Laure Boulesteix, Matthias Schmid, 2016, Biometrics.

Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations

Carolin Strobl, Andreas Bender, Anne-Laure Boulesteix, 2012, Briefings Bioinform..

Benchmarking for Clustering Methods Based on Real Data: A Statistical View

Anne-Laure Boulesteix, Myriam Hatz, A. Boulesteix, 2017 .

Which Resampling-Based Error Estimator for Benchmark Studies? A Power Analysis with Application to PLS-LDA

Anne-Laure Boulesteix, 2014 .

IPF-LASSO: Integrative L 1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data

Xiaoyu Jiang, Anne-Laure Boulesteix, Mathias Fuchs, 2017, Comput. Math. Methods Medicine.

Conditional variable importance for random forests

Achim Zeileis, Carolin Strobl, Anne-Laure Boulesteix, 2008, BMC Bioinformatics.

SHrinkage Covariance Estimation Incorporating Prior Biological Knowledge with Applications to High-Dimensional Data

Anne-Laure Boulesteix, Arthur Tenenhaus, Vincent Guillemot, 2011 .

Added predictive value of high-throughput molecular data to clinical data and its validation

Anne-Laure Boulesteix, Willi Sauerbrei, A. Boulesteix, 2011, Briefings Bioinform..

On Fishing for Significance and Statistician’s Degree of Freedom in the Era of Big Molecular Data

Roman Hornung, Anne-Laure Boulesteix, Willi Sauerbrei, 2017 .

A Plea for Neutral Comparison Studies in Computational Sciences

Anne-Laure Boulesteix, Manuel J. A. Eugster, A. Boulesteix, 2012, PloS one.

On the necessity and design of studies comparing statistical methods

Anne-Laure Boulesteix, Harald Binder, Willi Sauerbrei, 2018, Biometrical journal. Biometrische Zeitschrift.

AUC-RF: A New Strategy for Genomic Profiling with Random Forest

Anne-Laure Boulesteix, M. L. Calle, Victor Urrea, 2011, Human Heredity.

On the Simultaneous Analysis of Clinical and Omics Data: A Comparison of Globalboosttest and Pre-validation Techniques

Anne-Laure Boulesteix, Margret-Ruth Oelker, A. Boulesteix, 2013, Statistical Models for Data Analysis.

Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies

Rory Wilson, Anne-Laure Boulesteix, Alexander Hapfelmeier, 2017, BMC Medical Research Methodology.

iPACOSE: an iterative algorithm for the estimation of gene regulation networks

Andreas Bender, Anne-Laure Boulesteix, Vincent Guillemot, 2012 .

Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation

Anne-Laure Boulesteix, Simone Wahl, Astrid Zierer, 2016, BMC Medical Research Methodology.

Random forest versus logistic regression: a large-scale benchmark experiment

Philipp Probst, Anne-Laure Boulesteix, Raphaël Couronné, 2018, BMC Bioinformatics.

Hyperparameters and tuning strategies for random forest

Philipp Probst, Anne-Laure Boulesteix, Marvin N. Wright, 2018, WIREs Data Mining Knowl. Discov..

Unbiased split selection for classification trees based on the Gini Index

Carolin Strobl, Anne-Laure Boulesteix, Thomas Augustin, 2007, Comput. Stat. Data Anal..

Investigating the prediction ability of survival models based on both clinical and omics data: two case studies

Anne-Laure Boulesteix, Willi Sauerbrei, Riccardo De Bin, 2014, Statistics in medicine.

Ten Simple Rules for Reducing Overoptimistic Reporting in Methodological Computational Research

Anne-Laure Boulesteix, A. Boulesteix, 2015, PLoS Comput. Biol..

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Anne-Laure Boulesteix, Silke Janitza, Jochen Kruppa, 2012, WIREs Data Mining Knowl. Discov..

Guest Editorial—Special Collection Topic: Statistical Systems Theory in Cancer Modeling, Diagnosis, and Therapy

Anne-Laure Boulesteix, Lori A Dalton, Edward R Dougherty, 2018, Cancer informatics.

CMA – a comprehensive Bioconductor package for supervised classification with high dimensional data

Anne-Laure Boulesteix, Martin Daumer, Martin Slawski, 2008, BMC Bioinformatics.

Added predictive value of omics data: specific issues related to validation illustrated by two case studies

Anne-Laure Boulesteix, Riccardo De Bin, Tobias Herold, 2014, BMC Medical Research Methodology.

A Framework to Discover Emerging Patterns for Application in Microarray Data

Gerhard Tutz, Anne-Laure Boulesteix, A. Boulesteix, 2003 .

WilcoxCV: an R package for fast variable selection in cross-validation

Anne-Laure Boulesteix, A. Boulesteix, 2007, Bioinform..

Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning

Bernd Bischl, Roman Hornung, Anne-Laure Boulesteix, 2020 .

Use of pretransformation to cope with extreme values in important candidate features

Anne-Laure Boulesteix, Willi Sauerbrei, Vincent Guillemot, 2011, Biometrical journal. Biometrische Zeitschrift.

Over-optimism in bioinformatics research

Anne-Laure Boulesteix, A. Boulesteix, 2010, Bioinform..

Essential guidelines for computational method benchmarking

Yvan Saeys, Anne-Laure Boulesteix, Robrecht Cannoodt, 2018, Genome Biology.

Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data

Roman Hornung, Vindi Jurinovic, Anne-Laure Boulesteix, 2018, BMC Bioinformatics.

A measure of the impact of CV incompleteness on prediction error estimation with application to PCA and normalization

Rory Wilson, Roman Hornung, Caroline Truntzer, 2015, BMC Medical Research Methodology.

Testing the additional predictive value of high-dimensional molecular data

Torsten Hothorn, Anne-Laure Boulesteix, A. Boulesteix, 2010, BMC Bioinformatics.

Bias in random forest variable importance measures: Illustrations, sources and a solution

Achim Zeileis, Carolin Strobl, Torsten Hothorn, 2007, BMC Bioinformatics.

Complexity Selection with Cross-validation for Lasso and Sparse Partial Least Squares Using High-Dimensional Data

Anne-Laure Boulesteix, Christoph Bernau, Adrian Richter, 2013, Algorithms from and for Nature and Life.

A Statistical Framework for Hypothesis Testing in Real Data Benchmark Experiments

Anne-Laure Boulesteix, 2015 .

On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models

Anne-Laure Boulesteix, Christoph Bernau, Riccardo De Bin, 2018, Comput. Stat..

Random forest for ordinal responses: Prediction and variable selection

Gerhard Tutz, Anne-Laure Boulesteix, Silke Janitza, 2016, Comput. Stat. Data Anal..

Over-optimism in bioinformatics: an illustration

Anne-Laure Boulesteix, Arthur Tenenhaus, Korbinian Strimmer, 2010, Bioinform..

Tunability: Importance of Hyperparameters of Machine Learning Algorithms

Bernd Bischl, Philipp Probst, Anne-Laure Boulesteix, 2018, J. Mach. Learn. Res..

Detection of influential points as a byproduct of resampling-based variable selection procedures

Anne-Laure Boulesteix, Willi Sauerbrei, Riccardo De Bin, 2017, Comput. Stat. Data Anal..

To tune or not to tune the number of trees in random forest?

Philipp Probst, Anne-Laure Boulesteix, A. Boulesteix, 2017, J. Mach. Learn. Res..

Full versus incomplete cross-validation: measuring the impact of imperfect separation between training and test sets in prediction error estimation

Roman Hornung, Caroline Truntzer, Anne-Laure Boulesteix, 2014 .

Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment

David Causeur, Roman Hornung, Anne-Laure Boulesteix, 2016, BMC Bioinformatics.

Pitfalls of hypothesis tests and model selection on bootstrap samples: Causes and consequences in biometrical applications

Anne-Laure Boulesteix, Harald Binder, Silke Janitza, 2016, Biometrical journal. Biometrische Zeitschrift.

On representative and illustrative comparisons with real data in bioinformatics: response to the letter to the editor by Smith et al

Anne-Laure Boulesteix, A. Boulesteix, 2013, Bioinform..

Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction

Anne-Laure Boulesteix, Carolin Strobl, A. Boulesteix, 2009, BMC medical research methodology.

Stochastic modeling for the COMET-assay

Anne-Laure Boulesteix, Volkmar Liebscher, Volker Hösel, 2003 .

Improving cross‐study prediction through addon batch effect adjustment or addon normalization

David Causeur, Roman Hornung, Anne-Laure Boulesteix, 2016, Bioinform..

Regularized estimation of large-scale gene association networks using graphical Gaussian models

Anne-Laure Boulesteix, Juliane Schäfer, Nicole Krämer, 2009, BMC Bioinformatics.

Correcting the optimally selected resampling-based error rate: A smooth analytical alternative to nested cross-validation

Anne-Laure Boulesteix, Thomas Augustin, Christoph Bernau, 2011 .

Microarray-based classification and clinical predictors: on combined classifiers and additional predictive value

Anne-Laure Boulesteix, Martin Daumer, Christine Porzelius, 2008, Bioinform..

Making complex prediction rules applicable for readers: Current practice in random forest literature and recommendations

Philipp Probst, Roman Hornung, Anne-Laure Boulesteix, 2018, Biometrical journal. Biometrische Zeitschrift.

Subsampling versus bootstrapping in resampling‐based model selection for multivariable regression

Anne-Laure Boulesteix, Willi Sauerbrei, Silke Janitza, 2016, Biometrics.

Partial least squares: a versatile tool for the analysis of high-dimensional genomic data

Anne-Laure Boulesteix, Korbinian Strimmer, A. Boulesteix, 2006, Briefings Bioinform..

Cross-study validation for the assessment of prediction algorithms

Anne-Laure Boulesteix, Lorenzo Trippa, Curtis Huttenhower, 2014, Bioinform..

Identification of interaction patterns and classification with applications to microarray data

Gerhard Tutz, Anne-Laure Boulesteix, A. Boulesteix, 2006, Comput. Stat. Data Anal..

A computationally fast variable importance test for random forests for high-dimensional data

Anne-Laure Boulesteix, Silke Janitza, Ender Celik, 2015, Adv. Data Anal. Classif..

Stability and aggregation of ranked gene lists

Anne-Laure Boulesteix, Martin Slawski, A. Boulesteix, 2009, Briefings Bioinform..

An AUC-based permutation variable importance measure for random forests

Carolin Strobl, Anne-Laure Boulesteix, Silke Janitza, 2013, BMC Bioinformatics.

Interaction forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects

Roman Hornung, Anne-Laure Boulesteix, A. Boulesteix, 2021, Comput. Stat. Data Anal..

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges pdf

Bernd Bischl, Anne-Laure Boulesteix, Marius Lindauer, 2021, WIREs Data. Mining. Knowl. Discov..

Publication Bias in Methodological Computational Research

Anne-Laure Boulesteix, Alexander Hapfelmeier, Veronika Stierle, 2015, Cancer informatics.

Dimension reduction and Classification with High-Dimensional Microarray Data

Anne-Laure Boulesteix, A. Boulesteix, 2005 .

Microarray-based prediction of tumor response to neoadjuvant radiochemotherapy of patients with locally advanced rectal cancer.

Anne-Laure Boulesteix, Karen Becker, Robert Rosenberg, 2008, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.

Comments on: Augmenting the bootstrap to analyze high dimensional genomic data

Anne-Laure Boulesteix, Athanassios Kondylis, Nicole Krämer, 2008 .

Maximally Selected Chi‐square Statistics for Ordinal Variables

Anne-Laure Boulesteix, A. Boulesteix, 2006, Biometrical journal. Biometrische Zeitschrift.

Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives

Anne-Laure Boulesteix, Jens Brauer, Arndt Wilcke, 2016, Human Genetics.

Statistical Applications in Genetics and Molecular Biology Multiple Testing for SNP-SNP Interactions

Carolin Strobl, Anne-Laure Boulesteix, Stefan Wagenpfeil, 2007 .

Sampling uncertainty versus method uncertainty: A general framework with applications to omics biomarker selection

Anne-Laure Boulesteix, Simon Klau, Marie-Laure Martin-Magniette, 2019, Biometrical journal. Biometrische Zeitschrift.

Variable Selection and Parameter Tuning in High-Dimensional Prediction

Anne-Laure Boulesteix, Christoph Bernau, 2010 .

Categorical variables with many categories arepreferentially selected in model selection procedures for multivariable regression models on bootstrap samples

Anne-Laure Boulesteix, Silke Janitza, Susanne Rospleszcz, 2014 .

Machine learning versus statistical modeling

Anne-Laure Boulesteix, Matthias Schmid, A. Boulesteix, 2014, Biometrical journal. Biometrische Zeitschrift.

Penalized Partial Least Squares Based on B-Splines Transformations

Gerhard Tutz, Anne-Laure Boulesteix, Nicole Krämer, 2006 .

Reader's Reaction to "Dimension Reduction for Classification with Gene Expression Microarray Data" by Dai et al (2006)

Anne-Laure Boulesteix, A. Boulesteix, 2006, Statistical applications in genetics and molecular biology.

Random Forests for Ordinal Response Data: Prediction and Variable Selection

Gerhard Tutz, Anne-Laure Boulesteix, Silke Janitza, 2014 .

Validation in bioinformatics and molecular medicine. Editorial.

Anne-Laure Boulesteix, 2011, Briefings in bioinformatics.

An eight-gene expression signature for the prediction of survival and time to treatment in chronic lymphocytic leukemia

Anne-Laure Boulesteix, Ulrich Mansmann, Klaus H. Metzeler, 2011, Leukemia.

Maximally Selected Chi‐Square Statistics and Binary Splits of Nominal Variables

Anne-Laure Boulesteix, A. Boulesteix, 2006, Biometrical journal. Biometrische Zeitschrift.

A U-statistic estimator for the variance of resampling-based error estimators

Roman Hornung, Anne-Laure Boulesteix, Riccardo De Bin, 2013, 1310.8203.

A note on between-group PCA

Anne-Laure Boulesteix, A. Boulesteix, 2004 .

Stability Investigations of Multivariable Regression Models Derived from Low- and High-Dimensional Data

Anne-Laure Boulesteix, Harald Binder, Willi Sauerbrei, 2011, Journal of biopharmaceutical statistics.

On stability issues in deriving multivariable regression models

Anne-Laure Boulesteix, Harald Binder, Willi Sauerbrei, 2015, Biometrical journal. Biometrische Zeitschrift.

The Normal Fetal Heart Rate Study: Analysis Plan

Anne-Laure Boulesteix, Martin Daumer, Wolfgang Hatzmann, 2007 .

The Normal Fetal Heart Rate Study: Analysis Plan

Anne-Laure Boulesteix, Martin Daumer, Wolfgang Hatzmann, 2007 .

On the choice and influence of the number of boosting steps

Anne-Laure Boulesteix, Christoph Bernau, Riccardo De Bin, 2016 .

Categorical variables with many categories are preferentially selected in bootstrap‐based model selection procedures for multivariable regression models

Anne-Laure Boulesteix, Silke Janitza, A. Boulesteix, 2016, Biometrical journal. Biometrische Zeitschrift.