World's Best Scientists 2026 revealed!
Dietrich Rebholz-Schuhmann

Dietrich Rebholz-Schuhmann

D-Index & Metrics

Computer Science

D-Index
42
Citations
5669
World Ranking
8523
National Ranking
421

Dietrich Rebholz-Schuhmann 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 Dietrich Rebholz-Schuhmann 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: 178 publications — 38th percentile

38% of scientists in this discipline score the same or lower.

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

Dietrich Rebholz-Schuhmann 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 Dietrich Rebholz-Schuhmann 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Dietrich Rebholz-Schuhmann is affiliated with the University of Cologne in Germany. Their research spans multiple fields, with a focus on biochemistry, genetics, molecular biology, and computer science. The subfields of study prominently include molecular biology, artificial intelligence, information systems, information systems and management, and health informatics.

Their work prominently addresses several main topics, notably:

  • Bioinformatics and Genomic Networks
  • Biomedical Text Mining and Ontologies
  • Research Data Management Practices
  • Scientific Computing and Data Management
  • Semantic Web and Ontologies
  • Explainable Artificial Intelligence (XAI)
  • Artificial Intelligence in Healthcare and Education

Dietrich Rebholz-Schuhmann has co-authored publications with several frequent collaborators, including:

  • Leyla Jael Castro
  • Stefan Decker
  • Oya Beyan
  • Olga Giraldo
  • Michael Cochez

A significant portion of their research output has been published in various venues, where they have multiple contributions. These venues include:

  • Zenodo (CERN European Organization for Nuclear Research)
  • arXiv (Cornell University)
  • Proceedings of the Conference on Research Data Infrastructure
  • IEEE Access
  • Briefings in Bioinformatics

The scientist's recent papers demonstrate an interest in explainable artificial intelligence applied to bioinformatics and healthcare challenges. Notable publications include:

  • Explainable AI for Bioinformatics: Methods, Tools and Applications, 2023, Briefings in Bioinformatics
  • DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From Radiographs and Magnetic Resonance Imaging, 2021, IEEE Access
  • DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images, 2020, arXiv (Cornell University)
  • Adversary-Aware Multimodal Neural Networks for Cancer Susceptibility Prediction From Multiomics Data, 2022, IEEE Access
  • Explainable AI for Bioinformatics: Methods, Tools, and Applications, 2022, arXiv (Cornell University)

Best Publications

  • Text processing through Web services

    Dietrich Rebholz-Schuhmann;Miguel Arregui;Sylvain Gaudan;Harald Kirsch

  • Text-mining solutions for biomedical research: enabling integrative biology.

    Dietrich Rebholz-Schuhmann;Anika Oellrich;Robert Hoehndorf

  • Deep learning-based clustering approaches for bioinformatics

    Md. Rezaul Karim;Oya Beyan;Oya Beyan;Achille Zappa;Ivan G. Costa

  • EBIMed---text crunching to gather facts for proteins from Medline

    Dietrich Rebholz-Schuhmann;Harald Kirsch;Miguel Arregui;Sylvain Gaudan

  • Facts from text--is text mining ready to deliver?

    Dietrich Rebholz-Schuhmann;Harald Kirsch;Francisco Couto

  • Automatic recognition of conceptualization zones in scientific articles and two life science applications

    Maria Liakata;Shyamasree Saha;Simon Dobnik;Colin Batchelor

  • Assessment of disease named entity recognition on a corpus of annotated sentences

    Antonio Jimeno;Ernesto Jimenez-Ruiz;Vivian Lee;Sylvain Gaudan

  • Text mining for biology - the way forward: opinions from leading scientists

    Russ B. Altman;Casey M. Bergman;Judith A. Blake;Christian Blaschke

  • Resolving abbreviations to their senses in Medline

    S. Gaudan;H. Kirsch;D. Rebholz-Schuhmann

  • CALBC silver standard corpus.

    Dietrich Rebholz-Schuhmann;Antonio José Jimeno Yepes;Erik M Van Mulligen;Ning Kang

  • DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images

    Md. Rezaul Karim;Till Dohmen;Michael Cochez;Oya Beyan

  • MeSH Up

    Dolf Trieschnigg;Piotr Pezik;Vivian Lee;Franciska de Jong

  • Using argumentation to extract key sentences from biomedical abstracts.

    Patrick Ruch;Celia Boyer;Christine Chichester;Imad Tbahriti;Imad Tbahriti

  • Biological network extraction from scientific literature: state of the art and challenges

    Chen Li;Maria Liakata;Maria Liakata;Dietrich Rebholz-Schuhmann;Dietrich Rebholz-Schuhmann

  • Automatic extraction of mutations from Medline and cross‐validation with OMIM

    Dietrich Rebholz‐Schuhmann;Stephane Marcel;Sylvie Albert;Ralf Tolle

  • Ontology refinement for improved information retrieval

    Antonio Jimeno-Yepes;Rafael Berlanga-Llavori;Dietrich Rebholz-Schuhmann

  • GOAnnotator: linking protein GO annotations to evidence text

    Francisco M Couto;Mário J Silva;Vivian Lee;Emily Dimmer

  • Gene Regulation Ontology (GRO): Design Principles and Use Cases

    Elena Beisswanger;Vivian Lee;Jung-Jae Kim;Dietrich Rebholz-Schuhmann

  • Integrating protein-protein interactions and text mining for protein function prediction.

    Samira Jaeger;Samira Jaeger;Sylvain Gaudan;Ulf Leser;Dietrich Rebholz-Schuhmann

  • Relations as patterns: bridging the gap between OBO and OWL

    Robert Hoehndorf;Anika Oellrich;Michel Dumontier;Janet Kelso

  • Assessment of NER solutions against the first and second CALBC Silver Standard Corpus.

    Dietrich Rebholz-Schuhmann;Antonio Jimeno Yepes;Chen Li;Şenay Kafkas

  • DeepCOVIDExplainer: Explainable COVID-19 Predictions Based on Chest X-ray Images

    Md. Rezaul Karim;Till Döhmen;Dietrich Rebholz-Schuhmann;Stefan Decker

Frequent Co-Authors

Robert Hoehndorf
Robert Hoehndorf King Abdullah University of Science and Technology
Udo Hahn
Udo Hahn Friedrich Schiller University Jena
Nigel Collier
Nigel Collier University of Cambridge
Antonio Jimeno Yepes
Antonio Jimeno Yepes RMIT University
Erik M. van Mulligen
Erik M. van Mulligen Erasmus University Rotterdam
Goran Nenadic
Goran Nenadic University of Manchester
Ernesto Jiménez-Ruiz
Ernesto Jiménez-Ruiz City, University of London
Georgios V. Gkoutos
Georgios V. Gkoutos University of Birmingham
Maria Liakata
Maria Liakata Queen Mary University of London
Francisco M. Couto
Francisco M. Couto University of Lisbon

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