World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
5677
World Ranking
12530
National Ranking
793

Goran Nenadic 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 Goran Nenadic 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: 281 publications — 70th percentile

70% of scientists in this discipline score the same or lower.

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

Goran Nenadic 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 Goran Nenadic 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Best Publications

  • Machine learning methods for wind turbine condition monitoring: A review

    Adrian Stetco;Fateme Dinmohammadi;Xingyu Zhao;Valentin Robu

  • Term identification in the biomedical literature

    Michael Krauthammer;Goran Nenadic

  • LINNAEUS: A species name identification system for biomedical literature

    Martin Gerner;Goran Nenadic;Casey M Bergman

  • Clinical Text Data in Machine Learning: Systematic Review.

    Irena Spasic;Goran Nenadic

  • #WhyWeTweetMH: Understanding Why People Use Twitter to Discuss Mental Health Problems.

    Natalie Berry;Fiona Lobban;Maksim Belousov;Richard Emsley

  • Text mining of cancer-related information: review of current status and future directions

    Irena Spasić;Jacqueline Livsey;John A. Keane;Goran Nenadić

  • Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives

    Aleksandar Kovačević;Azad Dehghan;Michele Filannino;John A Keane

  • A Text Mining Approach to the Prediction of Disease Status from Clinical Discharge Summaries

    Hui Yang;Irena Spasic;John A. Keane;Goran Nenadic

  • The GNAT library for local and remote gene mention normalization

    Jörg Hakenberg;Martin Gerner;Maximilian Haeussler;Illés Solt

  • Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task.

    Abeed Sarker;Maksim Belousov;Jasper Friedrichs;Kai Hakala

  • Medication information extraction with linguistic pattern matching and semantic rules

    Irena Spasić;Farzaneh Sarafraz;John A Keane;Goran Nenadić

  • A Parallel Distributed Weka Framework for Big Data Mining Using Spark

    Aris-Kyriakos Koliopoulos;Paraskevas Yiapanis;Firat Tekiner;Goran Nenadic

  • BioContext: an integrated text mining system for large-scale extraction and contextualization of biomolecular events

    Martin Gerner;Farzaneh Sarafraz;Casey M. Bergman;Goran Nenadic

  • Automatic discovery of term similarities using pattern mining

    Goran Nenadić;Irena Spasić;Sophia Ananiadou

  • Terminology-driven mining of biomedical literature

    Goran Nenadić;Irena Spasić;Sophia Ananiadou

  • Combining knowledge- and data-driven methods for de-identification of clinical narratives

    Azad Dehghan;Aleksandar Kovacevic;George Karystianis;John A. Keane

  • Mining protein function from text using term-based support vector machines.

    Simon B Rice;Goran Nenadic;Benjamin J Stapley

  • Should free-text data in electronic medical records be shared for research? A citizens' jury study in the UK.

    Elizabeth Ford;Malcolm Oswald;Lamiece Hassan;Kyle Bozentko

  • A survey of bioinformatics database and software usage through mining the literature

    Geraint Duck;Goran Nenadic;Michele Filannino;Andy Brass

  • Terminology-driven literature mining and knowledge acquisition in biomedicine

    Goran Nenadić;Hideki Mima;Irena Spasić;Sophia Ananiadou

Frequent Co-Authors

Sophia Ananiadou
Sophia Ananiadou University of Manchester
David Robertson
David Robertson Vanderbilt University
Casey M. Bergman
Casey M. Bergman University of Georgia
Xiao-Jun Zeng
Xiao-Jun Zeng University of Manchester
Dietrich Rebholz-Schuhmann
Dietrich Rebholz-Schuhmann University of Cologne
Alan Jackson
Alan Jackson Arizona State University
Tony Butler
Tony Butler University of New South Wales
Jun'ichi Tsujii
Jun'ichi Tsujii University of Manchester
Alan D Radford
Alan D Radford University of Liverpool
Mike Barnes
Mike Barnes University of Manchester

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Best Scientists Citing Goran Nenadic