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Anne-Laure Boulesteix
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- affiliation: LMU Munich, Institute for Medical Information Processing, Biometry and Epidemiology, Germany
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2020 – today
- 2024
- [j50]Maximilian M. Mandl
, Sabine Hoffmann, Sebastian Bieringer, Anna E. Jacob
Raising awareness of uncertain choices in empirical data analysis: A teaching concept toward replicable research practices. PLoS Comput. Biol. 20(3): 1011936 (2024) - [c1]Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl:
Position: Why We Must Rethink Empirical Research in Machine Learning. ICML 2024 - [i10]Lasai Barreñada, Paula Dhiman, Dirk Timmerman, Anne-Laure Boulesteix, Ben Van Calster:
Understanding random forests and overfitting: a visualization and simulation study. CoRR abs/2402.18612 (2024) - [i9]Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl:
Position: Why We Must Rethink Empirical Research in Machine Learning. CoRR abs/2405.02200 (2024) - [i8]Hannah Schulz-Kümpel, Sebastian Fischer, Thomas Nagler, Anne-Laure Boulesteix, Bernd Bischl, Roman Hornung:
Constructing Confidence Intervals for 'the' Generalization Error - a Comprehensive Benchmark Study. CoRR abs/2409.18836 (2024) - 2023
- [j49]Theresa Ullmann
Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study. Adv. Data Anal. Classif. 17(1): 211-238 (2023) - [j48]Theresa Ullmann
Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering. PLoS Comput. Biol. 19(1) (2023) - [j47]Bernd Bischl
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. WIREs Data. Mining. Knowl. Discov. 13(2) (2023) - [j46]Iven Van Mechelen
A white paper on good research practices in benchmarking: The case of cluster analysis. WIREs Data. Mining. Knowl. Discov. 13(6) (2023) - [i7]Roman Hornung, Frederik Ludwigs, Jonas Hagenberg, Anne-Laure Boulesteix:
Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study. CoRR abs/2302.03991 (2023) - [i6]Roman Hornung, Malte Nalenz, Lennart Schneider, Andreas Bender, Ludwig Bothmann, Bernd Bischl, Thomas Augustin, Anne-Laure Boulesteix:
Evaluating machine learning models in non-standard settings: An overview and new findings. CoRR abs/2310.15108 (2023) - 2022
- [j45]Roman Hornung
Interaction forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects. Comput. Stat. Data Anal. 171: 107460 (2022) - [j44]Christina Nießl
Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results. WIREs Data Mining Knowl. Discov. 12(2) (2022) - [j43]Theresa Ullmann
Validation of cluster analysis results on validation data: A systematic framework. WIREs Data Mining Knowl. Discov. 12(3) (2022) - 2021
- [j42]Moritz Herrmann
Large-scale benchmark study of survival prediction methods using multi-omics data. Briefings Bioinform. 22(3) (2021) - [j41]Stefanie Peschel, Christian L. Müller, Erika von Mutius, Anne-Laure Boulesteix, Martin Depner:
NetCoMi: network construction and comparison for microbiome data in R. Briefings Bioinform. 22(4) (2021) - [j40]Nicole Ellenbach
Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning. J. Classif. 38(2): 212-231 (2021) - [i5]Bernd Bischl, Martin Binder, Michel Lang
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. CoRR abs/2107.05847 (2021) - 2020
- [j39]Riccardo De Bin
Combining clinical and molecular data in regression prediction models: insights from a simulation study. Briefings Bioinform. 21(6): 1904-1919 (2020) - [i4]Moritz Herrmann, Philipp Probst, Roman Hornung, Vindi Jurinovic, Anne-Laure Boulesteix:
Large-scale benchmark study of survival prediction methods using multi-omics data. CoRR abs/2003.03621 (2020)
2010 – 2019
- 2019
- [j38]Philipp Probst, Anne-Laure Boulesteix, Bernd Bischl:
Tunability: Importance of Hyperparameters of Machine Learning Algorithms. J. Mach. Learn. Res. 20: 53:1-53:32 (2019) - [j37]Philipp Probst, Marvin N. Wright
Hyperparameters and tuning strategies for random forest. WIREs Data Mining Knowl. Discov. 9(3) (2019) - 2018
- [j36]Silke Janitza
A computationally fast variable importance test for random forests for high-dimensional data. Adv. Data Anal. Classif. 12(4): 885-915 (2018) - [j35]Raphaël Couronné, Philipp Probst, Anne-Laure Boulesteix
Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinform. 19(1): 270:1-270:14 (2018) - [j34]Simon Klau
Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data. BMC Bioinform. 19(1): 322:1-322:14 (2018) - [j33]Heidi Seibold
On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models. Comput. Stat. 33(3): 1195-1215 (2018) - [i3]Philipp Probst, Marvin N. Wright, Anne-Laure Boulesteix:
Hyperparameters and Tuning Strategies for Random Forest. CoRR abs/1804.03515 (2018) - 2017
- [j32]Roman Hornung, David Causeur, Christoph Bernau, Anne-Laure Boulesteix
Improving cross-study prediction through addon batch effect adjustment or addon normalization. Bioinform. 33(3): 397-404 (2017) - [j31]Anne-Laure Boulesteix
IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data. Comput. Math. Methods Medicine 2017: 7691937:1-7691937:14 (2017) - [j30]Riccardo De Bin
Detection of influential points as a byproduct of resampling-based variable selection procedures. Comput. Stat. Data Anal. 116: 19-31 (2017) - [j29]Philipp Probst, Anne-Laure Boulesteix:
To Tune or Not to Tune the Number of Trees in Random Forest. J. Mach. Learn. Res. 18: 181:1-181:18 (2017) - [i2]Philipp Probst, Anne-Laure Boulesteix:
To tune or not to tune the number of trees in random forest? CoRR abs/1705.05654 (2017) - 2016
- [j28]Roman Hornung, Anne-Laure Boulesteix
Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinform. 17: 27 (2016) - [j27]Silke Janitza
Random forest for ordinal responses: Prediction and variable selection. Comput. Stat. Data Anal. 96: 57-73 (2016) - 2015
- [j26]Anne-Laure Boulesteix
Letter to the Editor: On the term 'interaction' and related phrases in the literature on Random Forests. Briefings Bioinform. 16(2): 338-345 (2015) - [j25]Anne-Laure Boulesteix
Letter to the Editor: On Reviews and Papers on New Methods. Briefings Bioinform. 16(2): 365-366 (2015) - [j24]Anne-Laure Boulesteix
Ten Simple Rules for Reducing Overoptimistic Reporting in Methodological Computational Research. PLoS Comput. Biol. 11(4) (2015) - 2014
- [j23]Christoph Bernau, Markus Riester
Cross-study validation for the assessment of prediction algorithms. Bioinform. 30(12): 105-112 (2014) - 2013
- [j22]Anne-Laure Boulesteix
On representative and illustrative comparisons with real data in bioinformatics: response to the letter to the editor by Smith et al.. Bioinform. 29(20): 2664-2666 (2013) - [j21]Silke Janitza, Carolin Strobl
An AUC-based permutation variable importance measure for random forests. BMC Bioinform. 14: 119 (2013) - [p2]Margret-Ruth Oelker, Anne-Laure Boulesteix:
On the Simultaneous Analysis of Clinical and Omics Data: A Comparison of Globalboosttest and Pre-validation Techniques. Statistical Models for Data Analysis 2013: 259-267 - [p1]Anne-Laure Boulesteix, Adrian Richter, Christoph Bernau:
Complexity Selection with Cross-validation for Lasso and Sparse Partial Least Squares Using High-Dimensional Data. Algorithms from and for Nature and Life 2013: 261-268 - 2012
- [j20]Anne-Laure Boulesteix
Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations. Briefings Bioinform. 13(3): 292-304 (2012) - [j19]Anne-Laure Boulesteix
Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. WIREs Data Mining Knowl. Discov. 2(6): 493-507 (2012) - [i1]Anne-Laure Boulesteix, Manuel J. A. Eugster:
A Plea for Neutral Comparison Studies in Computational Sciences. CoRR abs/1208.2651 (2012) - 2011
- [j18]Anne-Laure Boulesteix
Editorial. Briefings Bioinform. 12(3): 187-188 (2011) - [j17]Anne-Laure Boulesteix
Added predictive value of high-throughput molecular data to clinical data and its validation. Briefings Bioinform. 12(3): 215-229 (2011) - 2010
- [j16]Anne-Laure Boulesteix
Over-optimism in bioinformatics research. Bioinform. 26(3): 437-439 (2010) - [j15]Monika Jelizarow, Vincent Guillemot, Arthur Tenenhaus, Korbinian Strimmer
Over-optimism in bioinformatics: an illustration. Bioinform. 26(16): 1990-1998 (2010) - [j14]Anne-Laure Boulesteix
Testing the additional predictive value of high-dimensional molecular data. BMC Bioinform. 11: 78 (2010)
2000 – 2009
- 2009
- [j13]Anne-Laure Boulesteix
Stability and aggregation of ranked gene lists. Briefings Bioinform. 10(5): 556-568 (2009) - [j12]Nicole Krämer
Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinform. 10: 384 (2009) - [j11]Wessel N. van Wieringen
Survival prediction using gene expression data: A review and comparison. Comput. Stat. Data Anal. 53(5): 1590-1603 (2009) - 2008
- [j10]Anne-Laure Boulesteix
Microarray-based classification and clinical predictors: on combined classifiers and additional predictive value. Bioinform. 24(15): 1698-1706 (2008) - [j9]Martin Slawski, Martin Daumer, Anne-Laure Boulesteix
CMA - a comprehensive Bioconductor package for supervised classification with high dimensional data. BMC Bioinform. 9 (2008) - [j8]Carolin Strobl
Conditional variable importance for random forests. BMC Bioinform. 9 (2008) - 2007
- [j7]Anne-Laure Boulesteix, Korbinian Strimmer:
Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Briefings Bioinform. 8(1): 32-44 (2007) - [j6]Anne-Laure Boulesteix
WilcoxCV: an R package for fast variable selection in cross-validation. Bioinform. 23(13): 1702-1704 (2007) - [j5]Carolin Strobl
Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinform. 8 (2007) - [j4]Anne-Laure Boulesteix
Maximally selected Chi-squared statistics and non-monotonic associations: An exact approach based on two cutpoints. Comput. Stat. Data Anal. 51(12): 6295-6306 (2007) - [j3]Carolin Strobl
Unbiased split selection for classification trees based on the Gini Index. Comput. Stat. Data Anal. 52(1): 483-501 (2007) - 2006
- [j2]Anne-Laure Boulesteix
Identification of interaction patterns and classification with applications to microarray data. Comput. Stat. Data Anal. 50(3): 783-802 (2006) - 2003
- [j1]Anne-Laure Boulesteix
A CART-based approach to discover emerging patterns in microarray data. Bioinform. 19(18): 2465-2472 (2003)
Coauthor Index
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last updated on 2024-12-10 21:46 CET by the dblp team
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