World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
7803
World Ranking
6566
National Ranking
26

Robert Hoehndorf 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 Robert Hoehndorf 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: 240 publications — 59th percentile

59% of scientists in this discipline score the same or lower.

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

Robert Hoehndorf 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 Robert Hoehndorf 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: 47 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Overview

Robert Hoehndorf is affiliated with King Abdullah University of Science and Technology in Saudi Arabia. Their research primarily spans the fields of Biochemistry, Genetics and Molecular Biology as well as Computer Science, with a significant focus on subfields such as Molecular Biology, Artificial Intelligence, and Genetics.

Their work covers several specialized areas including Biomedical Text Mining and Ontologies, Bioinformatics and Genomic Networks, Semantic Web and Ontologies, Genomics and Rare Diseases, Topic Modeling, Machine Learning in Bioinformatics, and Natural Language Processing Techniques.

Notable recent publications by Robert Hoehndorf include:

  • Protein function prediction as approximate semantic entailment, 2024, Nature Machine Intelligence
  • DeepGOPlus: improved protein function prediction from sequence, 2021, Computer applications in the biosciences
  • Semantic similarity and machine learning with ontologies, 2020, Briefings in Bioinformatics
  • DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms, 2022, Bioinformatics
  • DeepViral: prediction of novel virus-host interactions from protein sequences and infectious disease phenotypes, 2021, Bioinformatics

Robert Hoehndorf frequently collaborates with several co-authors, including Maxat Kulmanov, Georgios V. Gkoutos, Paul N. Schofield, Şenay Kafkas, and Fernando Zhapa-Camacho. These collaborations have produced a large body of work supporting advances in bioinformatics and related computational methods.

Their publications appear regularly in venues such as bioRxiv (Cold Spring Harbor Laboratory), arXiv (Cornell University), Bioinformatics, Journal of Biomedical Semantics, and Zenodo (CERN European Organization for Nuclear Research).

Best Publications

  • DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

    Maxat Kulmanov;Mohammed Asif Khan;Robert Hoehndorf

  • The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

    Naihui Zhou;Yuxiang Jiang;Timothy R. Bergquist;Alexandra J. Lee

  • FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration.

    Damion M Dooley;Emma J Griffiths;Emma J Griffiths;Gurinder S Gosal;Pier Luigi Buttigieg

  • The role of ontologies in biological and biomedical research: a functional perspective

    Robert Hoehndorf;Paul N. Schofield;Georgios V. Gkoutos

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

    Dietrich Rebholz-Schuhmann;Anika Oellrich;Robert Hoehndorf

  • The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery

    Michel Dumontier;Michel Dumontier;Christopher J. O. Baker;Joachim Baran;Alison Callahan

  • DeepGOPlus: improved protein function prediction from sequence.

    Maxat Kulmanov;Robert Hoehndorf

  • PhenomeNET: a whole-phenome approach to disease gene discovery

    Robert Hoehndorf;Paul Schofield;Georgios Vasileios Gkoutos

  • General Formal Ontology (GFO) - A Foundational Ontology Integrating Objects and Processes [Version 1.0]

    Heinrich Herre;Barbara Heller;Patryk Burek;Robert Hoehndorf

  • Neuro-symbolic representation learning on biological knowledge graphs.

    Mona Alshahrani;Mohammad Asif Khan;Omar Maddouri;Omar Maddouri;Akira R Kinjo

  • Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics.

    Martin Hrabě de Angelis;George Nicholson;Mohammed Selloum;Jacqueline K White

  • Semantic similarity and machine learning with ontologies

    Maxat Kulmanov;Fatima Zohra Smaili;Xin Gao;Robert Hoehndorf

  • OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction.

    Fatima Zohra Smaili;Xin Gao;Robert Hoehndorf

  • Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference

    Shichao Pei;Lu Yu;Robert Hoehndorf;Xiangliang Zhang

  • The Units Ontology: a tool for integrating units of measurement in science

    Georgios V. Gkoutos;Paul N. Schofield;Robert Hoehndorf

  • Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations.

    Fatima Zohra Smaili;Xin Gao;Robert Hoehndorf

  • Protein function prediction as approximate semantic entailment

    Unknown

  • The anatomy of phenotype ontologies: principles, properties and applications.

    Georgios V Gkoutos;Paul N Schofield;Robert Hoehndorf

  • Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases

    Robert Hoehndorf;Paul N. Schofield;Georgios V. Gkoutos;Georgios V. Gkoutos

  • Evaluation of research in biomedical ontologies

    Robert Hoehndorf;Michel Dumontier;Georgios V. Gkoutos

  • Aber-OWL: a framework for ontology-based data access in biology.

    Robert Hoehndorf;Luke T. Slater;Luke T. Slater;Paul N. Schofield;Georgios V. Gkoutos

  • EL Embeddings: Geometric Construction of Models for the Description Logic EL++

    Maxat Kulmanov;Wang Liu-Wei;Yuan Yan;Robert Hoehndorf

Frequent Co-Authors

Georgios V. Gkoutos
Georgios V. Gkoutos University of Birmingham
Paul N. Schofield
Paul N. Schofield University of Cambridge
Michel Dumontier
Michel Dumontier Maastricht University
Janet Kelso
Janet Kelso Max Planck Society
Dietrich Rebholz-Schuhmann
Dietrich Rebholz-Schuhmann University of Cologne
Xin Gao
Xin Gao King Abdullah University of Science and Technology
Takashi Gojobori
Takashi Gojobori King Abdullah University of Science and Technology
John P. Sundberg
John P. Sundberg Vanderbilt University
Vladimir B. Bajic
Vladimir B. Bajic King Abdullah University of Science and Technology
Axel-Cyrille Ngonga Ngomo
Axel-Cyrille Ngonga Ngomo University of Paderborn

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