World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
40
Citations
8100
World Ranking
7217
National Ranking
251

Davide Ballabio 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 Davide Ballabio 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: 135 publications — 21st percentile

21% of scientists in this discipline score the same or lower.

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

Davide Ballabio 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 Davide Ballabio 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: 40 D-Index — 27th percentile

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

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

Overview

Davide Ballabio is affiliated with the University of Milano-Bicocca in Italy. Their research activity spans multiple fields primarily related to chemistry, with a strong focus on analytical chemistry and computational methods applied to chemical and biological data.

The main field of study for Ballabio is Chemistry, with a total of 34 publications. Subfields include Analytical Chemistry, Computational Theory and Mathematics, Molecular Biology, Spectroscopy, and Biomedical Engineering. This multidisciplinary background supports diverse research themes involving both experimental and computational approaches.

Key research topics covered in their published work consist of:

  • Computational Drug Discovery Methods
  • Spectroscopy and Chemometric Analyses
  • Analytical Chemistry and Chromatography
  • Advanced Chemical Sensor Technologies
  • Metabolomics and Mass Spectrometry Studies
  • Machine Learning in Materials Science
  • Spectroscopy Techniques in Biomedical and Chemical Research

They have contributed to publications in a variety of scientific venues. The most frequent venues for Ballabio's work include:

  • Chemometrics and Intelligent Laboratory Systems
  • Zenodo (CERN European Organization for Nuclear Research)
  • Environmental Health Perspectives
  • Molecules
  • Separations

Ballabio's recent scientific papers demonstrate a breadth of topics and collaboration. Some of the notable publications are:

  • CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity (2020) in Environmental Health Perspectives
  • CATMoS: Collaborative Acute Toxicity Modeling Suite (2021) in Environmental Health Perspectives
  • Geographical identification of Chianti red wine based on ICP-MS element composition (2020) in Food Chemistry
  • Self-Organizing Map and Relational Perspective Mapping for the Accurate Visualization of High-Dimensional Hyperspectral Data (2020) in Analytical Chemistry
  • A MATLAB toolbox for multivariate regression coupled with variable selection (2021) in Chemometrics and Intelligent Laboratory Systems

Their frequent co-authors reflect an active collaboration network including:

  • Viviana Consonni
  • Roberto Todeschini
  • Francesca Grisoni
  • Cecile Valsecchi
  • Fabio Gosetti

Best Publications

  • Classification tools in chemistry. Part 1: linear models. PLS-DA

    Davide Ballabio;Viviana Consonni

  • Comparison of different approaches to define the applicability domain of QSAR models.

    F Sahigara;K Mansouri;D Ballabio;A Mauri

  • Evaluation of model predictive ability by external validation techniques

    Viviana Consonni;Davide Ballabio;Roberto Todeschini

  • Multivariate comparison of classification performance measures

    Davide Ballabio;Francesca Grisoni;Francesca Grisoni;Roberto Todeschini

  • Quantitative structure-activity relationship models for ready biodegradability of chemicals.

    Kamel Mansouri;Tine Ringsted;Davide Ballabio;Roberto Todeschini

  • A MATLAB toolbox for Principal Component Analysis and unsupervised exploration of data structure

    Davide Ballabio

  • Evaluation of different storage conditions of extra virgin olive oils with an innovative recognition tool built by means of electronic nose and electronic tongue

    M. S. Cosio;D. Ballabio;S. Benedetti;Carmelina Gigliotti

  • Geographical origin and authentication of extra virgin olive oils by an electronic nose in combination with artificial neural networks

    Maria S. Cosio;Davide Ballabio;Simona Benedetti;Carmelina Gigliotti

  • Prediction of Italian red wine sensorial descriptors from electronic nose, electronic tongue and spectrophotometric measurements by means of Genetic Algorithm regression models

    S. Buratti;D. Ballabio;S. Benedetti;M.S. Cosio

  • CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

    Kamel Mansouri;Nicole Kleinstreuer;Ahmed M. Abdelaziz;Domenico Alberga

  • Particle size, chemical composition, seasons of the year and urban, rural or remote site origins as determinants of biological effects of particulate matter on pulmonary cells.

    M.G. Perrone;Maurizio Gualtieri;V. Consonni;L. Ferrero

  • A MATLAB toolbox for Self Organizing Maps and supervised neural network learning strategies

    Davide Ballabio;Mahdi Vasighi

  • The Kohonen and CP-ANN toolbox: A collection of MATLAB modules for Self Organizing Maps and Counterpropagation Artificial Neural Networks

    Davide Ballabio;Viviana Consonni;Roberto Todeschini

  • Multivariate Classification for Qualitative Analysis

    Davide Ballabio;Roberto Todeschini

  • CATMoS: Collaborative Acute Toxicity Modeling Suite.

    Kamel Mansouri;Agnes L. Karmaus;Jeremy Fitzpatrick;Grace Patlewicz

  • Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions

    Faizan Sahigara;Davide Ballabio;Roberto Todeschini;Viviana Consonni

  • Beware of Unreliable Q2! A Comparative Study of Regression Metrics for Predictivity Assessment of QSAR Models.

    Roberto Todeschini;Davide Ballabio;Francesca Grisoni

  • Amperometric electronic tongue for food analysis

    Matteo Scampicchio;Davide Ballabio;Alessandra Arecchi;Stella M. Cosio

  • A novel variable reduction method adapted from space-filling designs

    Davide Ballabio;Viviana Consonni;Andrea Mauri;Magalie Claeys-Bruno

  • Locally centred Mahalanobis distance: a new distance measure with salient features towards outlier detection.

    Roberto Todeschini;Davide Ballabio;Viviana Consonni;Faizan Sahigara

  • Classification of GC‐MS measurements of wines by combining data dimension reduction and variable selection techniques

    Davide Ballabio;Thomas Hjort Skov;Riccardo Leardi;Rasmus Bro

  • Genetic Algorithms for architecture optimisation of Counter-Propagation Artificial Neural Networks

    Davide Ballabio;Mahdi Vasighi;Viviana Consonni;Mohsen Kompany-Zareh

  • Machine Learning Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project.

    Francesca Grisoni;Viviana Consonni;Davide Ballabio

  • Integrated QSAR Models to Predict Acute Oral Systemic Toxicity

    Davide Ballabio;Francesca Grisoni;Viviana Consonni;Roberto Todeschini

  • A QSTR-based expert system to predict sweetness of molecules

    Cristian Rojas;Roberto Todeschini;Davide Ballabio;Andrea Mauri

  • CAIMAN (Classification and Influence Matrix Analysis) : A new approach to the classification based on leverage-scaled functions

    R. Todeschini;D. Ballabio;V. Consonni;A. Mauri

  • Self-Organizing Map and Relational Perspective Mapping for the Accurate Visualization of High-Dimensional Hyperspectral Data.

    Wil Gardner;Wil Gardner;Ruqaya Maliki;Suzanne M. Cutts;Benjamin W. Muir

  • Development of models for predicting toxicity from sediment chemistry by partial least squares-discriminant analysis and counter-propagation artificial neural networks.

    Manuel Alvarez-Guerra;Davide Ballabio;José Manuel Amigo;Rasmus Bro

  • On the application of chemometrics for the study of acoustic-mechanical properties of crispy bakery products

    Laura Piazza;Jiabril Gigli;Davide Ballabio

Frequent Co-Authors

Roberto Todeschini
Roberto Todeschini University of Milano-Bicocca
Viviana Consonni
Viviana Consonni University of Milano-Bicocca
Eugene N. Muratov
Eugene N. Muratov University of North Carolina at Chapel Hill
Igor V. Tetko
Igor V. Tetko Helmholtz Zentrum München
Rasmus Bro
Rasmus Bro University of Copenhagen
Alexander Tropsha
Alexander Tropsha University of North Carolina at Chapel Hill
Denis Fourches
Denis Fourches North Carolina State University
Alexandre Varnek
Alexandre Varnek University of Strasbourg
Sean Ekins
Sean Ekins University of Arizona
Thomas Hartung
Thomas Hartung Johns Hopkins University

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