World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
53
Citations
19920
World Ranking
3306
National Ranking
100

Anne-Laure Boulesteix publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Anne-Laure Boulesteix sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 151 publications — 27th percentile

27% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Anne-Laure Boulesteix D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Anne-Laure Boulesteix sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 53 D-Index — 66th percentile

66% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Anne-Laure Boulesteix is affiliated with Ludwig-Maximilians-Universität München in Germany. Their research spans multiple fields, focusing primarily on Biochemistry, Genetics and Molecular Biology, as well as Computer Science.

Their key subfields of study include:

  • Molecular Biology
  • Artificial Intelligence
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Genetics

Research topics that have frequently appeared in their work include:

  • Bioinformatics and Genomic Networks
  • Gene expression and cancer classification
  • Meta-analysis and systematic reviews
  • Data Analysis with R
  • Machine Learning and Data Classification
  • Metabolomics and Mass Spectrometry Studies
  • Advanced Causal Inference Techniques

Boulesteix has contributed articles to a range of publication venues, showing consistent contributions particularly in:

  • arXiv (Cornell University)
  • Statistics in Medicine
  • Biometrical Journal
  • Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
  • bioRxiv (Cold Spring Harbor Laboratory)

Recent publications include:

  • TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods, 2024, BMJ
  • Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges, 2023, Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
  • NetCoMi: network construction and comparison for microbiome data in R, 2020, Briefings in Bioinformatics
  • Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI, 2022, Nature Medicine
  • Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease, 2022, European Heart Journal

The scientist collaborates frequently with other researchers. Notable frequent co-authors include:

  • Sabine Hoffmann
  • Theresa Ullmann
  • Roman Hornung
  • Moritz Herrmann
  • Tim P. Morris

Best Publications

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

    Carolin Strobl;Anne-Laure Boulesteix;Achim Zeileis;Torsten Hothorn

  • Conditional variable importance for random forests

    Carolin Strobl;Anne Laure Boulesteix;Thomas Kneib;Thomas Augustin

  • Hyperparameters and tuning strategies for random forest

    Philipp Probst;Marvin N. Wright;Anne-Laure Boulesteix

  • 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

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

    Anne-Laure Boulesteix;Korbinian Strimmer

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

    Raphael Couronné;Philipp Probst;Anne-Laure Boulesteix

  • Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges

    Bernd Bischl;Martin Binder;Michel Lang;Tobias Pielok

  • NetCoMi: network construction and comparison for microbiome data in R

    Stefanie Peschel;Christian L Müller;Erika von Mutius;Anne-Laure Boulesteix

  • Tunability: Importance of Hyperparameters of Machine Learning Algorithms

    Philipp Probst;Bernd Bischl;Anne-Laure Boulesteix

  • Unbiased Split Selection for Classification Trees based on the Gini Index

    Carolin Strobl;Anne-Laure Boulesteix;Thomas Augustin

  • An AUC-based permutation variable importance measure for random forests

    Silke Janitza;Carolin Strobl;Anne-Laure Boulesteix

  • PLS dimension reduction for classification with microarray data.

    Anne-Laure Boulesteix

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

    Nicole Krämer;Juliane Schäfer;Juliane Schäfer;Anne-Laure Boulesteix

  • Random forest for ordinal responses

    Silke Janitza;Gerhard Tutz;Anne-Laure Boulesteix

  • Tunability: Importance of Hyperparameters of Machine Learning Algorithms

    Philipp Probst;Anne-Laure Boulesteix;Bernd Bischl

  • Stability and aggregation of ranked gene lists

    Anne Laure Boulesteix;Martin Slawski

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

    Silke Janitza;Ender Celik;Anne-Laure Boulesteix

  • To Tune or Not to Tune the Number of Trees in Random Forest

    Philipp Probst;Anne-Laure Boulesteix

  • Survival prediction using gene expression data: A review and comparison

    Wessel N. van Wieringen;David Kun;Regina Hampel;Anne-Laure Boulesteix

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

    Nicole Kraemer;Juliane Schaefer;Anne-Laure Boulesteix

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

    Willi Sauerbrei;Anne-Laure Boulesteix;Harald Binder

  • A plea for neutral comparison studies in computational sciences.

    Anne-Laure Boulesteix;Sabine Lauer;Manuel J. A. Eugster;Manuel J. A. Eugster

  • Evaluating Microarray-based Classifiers: An Overview:

    Anne-Laure Boulesteix;Carolin Strobl;Thomas Augustin;Martin Daumer

Frequent Co-Authors

Gerhard Tutz
Gerhard Tutz Ludwig-Maximilians-Universität München
Mark D. Robinson
Mark D. Robinson University of Zurich
Korbinian Strimmer
Korbinian Strimmer University of Manchester
Bernd Bischl
Bernd Bischl Ludwig-Maximilians-Universität München
Wolfgang Hiddemann
Wolfgang Hiddemann Ludwig-Maximilians-Universität München
Michal Abrahamowicz
Michal Abrahamowicz McGill University
Heinz Höfler
Heinz Höfler Technical University of Munich
Achim Zeileis
Achim Zeileis University of Innsbruck
Rory P. Wilson
Rory P. Wilson Swansea University
Erika von Mutius
Erika von Mutius Ludwig-Maximilians-Universität München

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