World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
16483
World Ranking
10960
National Ranking
348

Giuseppe Jurman publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Giuseppe Jurman sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 150 publications — 27th percentile

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

The last bar groups every scientist with 991 publications or more.

Giuseppe Jurman D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Giuseppe Jurman sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Giuseppe Jurman is affiliated with the Fondazione Bruno Kessler in Italy, contributing extensively to several intersecting fields of scientific research. Their work primarily spans Biochemistry, Genetics and Molecular Biology, Medicine, and Computer Science, reflecting a multidisciplinary approach to complex biological and computational problems.

The main subfields of Giuseppe Jurman's research include Molecular Biology, Artificial Intelligence, Computer Vision and Pattern Recognition, Genetics, and Biophysics. This diverse expertise supports a broad investigation into biological systems and computational methods, emphasizing the integration of machine learning techniques with molecular and medical data analysis.

The scientist's recent papers address key topics in binary classification evaluation and machine learning applications in healthcare. Notable publications include:

  • The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation, 2020, BMC Genomics
  • The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, 2021, PeerJ Computer Science
  • The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation, 2021, BioData Mining
  • Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone, 2020, BMC Medical Informatics and Decision Making
  • The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification, 2023, BioData Mining

Key research topics covered in their work include gene expression and cancer classification, machine learning in healthcare, cell image analysis techniques, bioinformatics and genomic networks, imbalanced data classification techniques, artificial intelligence in healthcare, and AI in cancer detection.

The scientist frequently publishes in venues focused on computational biology and data mining, with submissions to:

  • BioData Mining (8 publications)
  • bioRxiv (Cold Spring Harbor Laboratory) (8 publications)
  • IEEE Access (4 publications)
  • Scientific Reports (4 publications)
  • Zenodo (CERN European Organization for Nuclear Research) (4 publications)

Collaboration has been a significant part of their research output, with frequent coauthors including Marco Chierici, Davide Chicco, Cesare Furlanello, Luca Coviello, and Nicole Bussola. These collaborations indicate a sustained network of researchers working within overlapping domains of computational science and biology.

Best Publications

  • The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

    Davide Chicco;Giuseppe Jurman

  • The Microarray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

    Leming Shi;Gregory Campbell;Wendell D. Jones;Fabien Campagne

  • The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation

    Davide Chicco;Niklas Tötsch;Giuseppe Jurman

  • Repeatability of published microarray gene expression analyses.

    John P A Ioannidis;David B Allison;Catherine A Ball;Issa Coulibaly

  • The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

    Charles Wang;Binsheng Gong;Pierre R. Bushel;Jean Thierry-Mieg

  • Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

    Davide Chicco;Giuseppe Jurman

  • The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification

    Unknown

  • A Comparison of MCC and CEN Error Measures in Multi-Class Prediction

    Giuseppe Jurman;Samantha Riccadonna;Cesare Furlanello

  • minerva and minepy

    Davide Albanese;Michele Filosi;Roberto Visintainer;Samantha Riccadonna

  • Entropy-based gene ranking without selection bias for the predictive classification of microarray data

    Cesare Furlanello;Maria Serafini;Stefano Merler;Giuseppe Jurman

  • Algebraic stability indicators for ranked lists in molecular profiling

    Giuseppe Jurman;Stefano Merler;Annalisa Barla;Silvano Paoli

  • Phylogenetic convolutional neural networks in metagenomics

    Diego Fioravanti;Diego Fioravanti;Ylenia Giarratano;Valerio Maggio;Claudio Agostinelli

  • An accelerated procedure for recursive feature ranking on microarray data

    C. Furlanello;M. Serafini;S. Merler;G. Jurman

  • The Benefits of the Matthews Correlation Coefficient (MCC) Over the Diagnostic Odds Ratio (DOR) in Binary Classification Assessment

    Davide Chicco;Valery Starovoitov;Giuseppe Jurman

  • Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders

    Nastaran Mohammadian Rad;Nastaran Mohammadian Rad;Nastaran Mohammadian Rad;Seyed Mostafa Kia;Calogero Zarbo;Twan van Laarhoven

  • Machine learning methods for predictive proteomics

    Annalisa Barla;Giuseppe Jurman;Samantha Riccadonna;Stefano Merler

  • Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events

    Gabriele Franch;Daniele Nerini;Marta Pendesini;Luca Coviello

  • mlpy: Machine Learning Python

    Davide Albanese;Roberto Visintainer;Stefano Merler;Samantha Riccadonna

  • Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes

    W. Shi;M. Bessarabova;D. Dosymbekov;Z. Dezso

  • A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

    Wendell Jones;Binsheng Gong;Natalia Novoradovskaya;Dan Li

  • Functional Analysis of Multiple Genomic Signatures Demonstrates that Classification Algorithms Choose Phenotype-Related Genes

    Weihua Shi;M. Bessarabova;D. Dosymbekov;Z. Dezso

  • Evaluating reproducibility of AI algorithms in digital pathology with DAPPER

    Andrea Bizzego;Andrea Bizzego;Nicole Bussola;Nicole Bussola;Marco Chierici;Valerio Maggio

  • The HIM glocal metric and kernel for network comparison and classification

    Giuseppe Jurman;Roberto Visintainer;Michele Filosi;Samantha Riccadonna

  • cmine, minerva & minepy: a C engine for the MINE suite and its R and Python wrappers

    Davide Albanese;Michele Filosi;Roberto Visintainer;Samantha Riccadonna

Frequent Co-Authors

Cesare Furlanello
Cesare Furlanello Fondazione Bruno Kessler
Stefano Merler
Stefano Merler Fondazione Bruno Kessler
Leming Shi
Leming Shi Fudan University
Weida Tong
Weida Tong National Center for Toxicological Research
Yuri Nikolsky
Yuri Nikolsky F1 Genomics
Julio Saez-Rodriguez
Julio Saez-Rodriguez Heidelberg University
Pierre R. Bushel
Pierre R. Bushel National Institutes of Health
Paola Venuti
Paola Venuti University of Trento
Joaquín Dopazo
Joaquín Dopazo Institute of Biomedicine of Seville

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