World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
5804
World Ranking
11223
National Ranking
185

Martin Krallinger 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 Martin Krallinger 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: 120 publications — 15th percentile

15% of scientists in this discipline score the same or lower.

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

Martin Krallinger 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 Martin Krallinger 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

Martin Krallinger is affiliated with the Barcelona Supercomputing Center in Spain. Their research primarily focuses on the intersection of computer science and biomedical applications, especially in the fields of artificial intelligence and molecular biology.

The scientist's work spans various topics, which include:

  • Biomedical Text Mining and Ontologies
  • Natural Language Processing Techniques
  • Topic Modeling
  • Linguistics and Terminology Studies
  • Semantic Web and Ontologies
  • Spanish Linguistics and Language Studies
  • Text Readability and Simplification

The main fields of study associated with Martin Krallinger are:

  • Computer Science

Within these fields, they have contributed significantly to several subfields, including:

  • Artificial Intelligence
  • Molecular Biology
  • Language and Linguistics
  • General Health Professions
  • Sociology and Political Science

Frequent collaborators in their research include:

  • Antonio Miranda-Escalada
  • Eulàlia Farré-Maduell
  • Salvador Lima López
  • Luis Gascó
  • Vicent Brivá-Iglesias

Martin Krallinger has published extensively in venues such as:

  • Zenodo (CERN European Organization for Nuclear Research)
  • OPAL (Open@LaTrobe) (La Trobe University)
  • arXiv (Cornell University)
  • Database
  • Nature Reviews Materials

Recent publications authored or co-authored by Martin Krallinger include:

  • Redefining biomaterial biocompatibility: challenges for artificial intelligence and text mining (2023, Trends in Biotechnology)
  • Time to kick-start text mining for biomaterials (2020, Nature Reviews Materials)
  • Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical-protein relations (2023, Database)
  • The Devices, Experimental Scaffolds, and Biomaterials Ontology (DEB): A Tool for Mapping, Annotation, and Analysis of Biomaterials Data (2020, Advanced Functional Materials)
  • Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers (2022, Database)

Best Publications

  • The CHEMDNER corpus of chemicals and drugs and its annotation principles.

    Martin Krallinger;Obdulia Rabal;Florian Leitner;Miguel Vazquez

  • Overview of the protein-protein interaction annotation extraction task of BioCreative II

    Martin Krallinger;Florian Leitner;Carlos Rodriguez-Penagos;Alfonso Valencia

  • Information retrieval and text mining technologies for chemistry

    Martin Krallinger;Obdulia Rabal;Anália Lourenço;Anália Lourenço;Julen Oyarzabal

  • Linking genes to literature: text mining, information extraction, and retrieval applications for biology

    Martin Krallinger;Alfonso Valencia;Lynette Hirschman

  • CHEMDNER: The drugs and chemical names extraction challenge

    Martin Krallinger;Florian Leitner;Obdulia Rabal;Miguel Vazquez

  • Text-mining and information-retrieval services for molecular biology

    Martin Krallinger;Alfonso Valencia

  • Evaluation of text-mining systems for biology: overview of the Second BioCreative community challenge

    Martin Krallinger;Alexander Morgan;Larry Smith;Florian Leitner

  • Text-mining approaches in molecular biology and biomedicine

    Martin Krallinger;Ramon Alonso-Allende Erhardt;Alfonso Valencia

  • BioC: a minimalist approach to interoperability for biomedical text processing

    Donald C. Comeau;Rezarta Islamaj Doğan;Paolo Ciccarese;Kevin Bretonnel Cohen

  • Text mining for the biocuration workflow

    Lynette Hirschman;Gully A. P. C. Burns;Martin Krallinger;Cecilia Arighi

  • The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text

    Martin Krallinger;Miguel Vazquez;Florian Leitner;David Salgado

  • Evaluation of BioCreAtIvE assessment of task 2.

    Christian Blaschke;Eduardo Andres Leon;Martin Krallinger;Alfonso Valencia

  • An Overview of BioCreative II.5

    Florian Leitner;Scott A. Mardis;Martin Krallinger;Gianni Cesareni

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

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

  • Overview of the BioCreative III Workshop

    Cecilia N. Arighi;Zhiyong Lu;Martin Krallinger;Kevin Bretonnel Cohen

  • Text Mining for Metabolic Pathways, Signaling Cascades, and Protein Networks

    Robert Hoffmann;Martin Krallinger;Eduardo Andres;Javier Tamames

  • Overview of the CLEF eHealth Evaluation Lab 2020

    Lorraine Goeuriot;Hanna Suominen;Hanna Suominen;Hanna Suominen;Liadh Kelly;Antonio Miranda-Escalada

  • Text Mining for Drugs and Chemical Compounds: Methods, Tools and Applications

    Miguel Vazquez;Martin Krallinger;Florian Leitner;Alfonso Valencia

  • Analysis of biological processes and diseases using text mining approaches.

    Martin Krallinger;Florian Leitner;Alfonso Valencia

  • BioCreative III interactive task: an overview.

    Cecilia N Arighi;Phoebe M Roberts;Shashank Agarwal;Sanmitra Bhattacharya

Frequent Co-Authors

Alfonso Valencia
Alfonso Valencia Barcelona Supercomputing Center
Lynette Hirschman
Lynette Hirschman Mitre (United States)
Zhiyong Lu
Zhiyong Lu National Institutes of Health
Georgios Paliouras
Georgios Paliouras National Centre of Scientific Research Demokritos
Andrew Chatr-aryamontri
Andrew Chatr-aryamontri University of Montreal
Cathy H. Wu
Cathy H. Wu University of Delaware
Florentino Fdez-Riverola
Florentino Fdez-Riverola Universidade de Vigo
W. John Wilbur
W. John Wilbur National Institutes of Health
Cecilia N. Arighi
Cecilia N. Arighi University of Delaware
Gianni Cesareni
Gianni Cesareni University of Rome Tor Vergata

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