World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
17712
World Ranking
13320
National Ranking
641

Stefan Kurtz 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 Stefan Kurtz 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: 70 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

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

Stefan Kurtz 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 Stefan Kurtz 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: 31 D-Index — 6th percentile

6% of scientists in this discipline score the same or lower.

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

Overview

Stefan Kurtz is affiliated with Universität Hamburg in Germany and works primarily in the field of Biochemistry, Genetics and Molecular Biology. Their research spans several subfields including Molecular Biology, Cancer Research, Reproductive Medicine, and Oncology.

The scientist's work focuses on topics such as RNA and protein synthesis mechanisms, protein structure and dynamics, machine learning applications in bioinformatics, genomics and phylogenetic studies, RNA research and splicing, and cancer genomics and diagnostics with particular attention to ovarian cancer diagnosis and treatment.

Stefan Kurtz has published several papers addressing diverse aspects of molecular biology and computational biology. Selected recent publications include:

  • Transcription factor prediction using protein 3D secondary structures (2024, Bioinformatics)
  • DeepGRP: engineering a software tool for predicting genomic repetitive elements using Recurrent Neural Networks with attention (2021, Algorithms for Molecular Biology)
  • The power and limits of predicting exon-exon interactions using protein 3D structures (2024, bioRxiv - Cold Spring Harbor Laboratory)
  • Loss of copy numbers of retrotransposons (HERVK) on chromosome 7p11.2 impacts EGFR (Epidermal Growth Factor Receptor)-induced phenotypes for platinum sensitivity and long-term survival in ovarian cancer-A study from the OVCAD consortium (2024, International Journal of Cancer)
  • OmixLitMiner 2: Guided Literature Mining Tools for Automated Categorization of Marker Candidates in Omics Studies (2025, bioRxiv - Cold Spring Harbor Laboratory)

The venues where Kurtz frequently publishes include:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Bioinformatics
  • Algorithms for Molecular Biology
  • International Journal of Cancer
  • PROTEOMICS

Among frequent collaborators, Stefan Kurtz has coauthored works with Jeanine Liebold, Jan Baumbach, Khalique Newaz, F. Neuhaus, and Janina Geiser.

Best Publications

  • Versatile and open software for comparing large genomes

    Stefan Kurtz;Adam Phillippy;Arthur L Delcher;Michael Smoot

  • REPuter: the manifold applications of repeat analysis on a genomic scale.

    Stefan Kurtz;Jomuna V. Choudhuri;Enno Ohlebusch;Chris Schleiermacher

  • LTRharvest , an efficient and flexible software for de novo detection of LTR retrotransposons

    David Ellinghaus;Stefan Kurtz;Ute Willhoeft

  • Replacing suffix trees with enhanced suffix arrays

    Mohamed Ibrahim Abouelhoda;Stefan Kurtz;Enno Ohlebusch

  • Fast Mapping of Short Sequences with Mismatches, Insertions and Deletions Using Index Structures

    Steve Hoffmann;Christian Otto;Stefan Kurtz;Cynthia Mira Sharma

  • REPuter: fast computation of maximal repeats in complete genomes.

    Stefan Kurtz;Chris Schleiermacher

  • Reducing the space requirement of suffix trees

    Stefan Kurtz

  • GenomeTools: A Comprehensive Software Library for Efficient Processing of Structured Genome Annotations

    Gordon Gremme;Sascha Steinbiss;Stefan Kurtz

  • Engineering a software tool for gene structure prediction in higher organisms

    Gordon Gremme;Volker Brendel;Michael E. Sparks;Stefan Kurtz

  • Fine-grained annotation and classification of de novo predicted LTR retrotransposons

    Sascha Steinbiss;Ute Willhoeft;Gordon Gremme;Stefan Kurtz

  • Local similarity in RNA secondary structures

    M. Hochsmann;T. Toller;R. Giegerich;S. Kurtz

  • A new method to compute K-mer frequencies and its application to annotate large repetitive plant genomes

    Stefan Kurtz;Apurva Narechania;Apurva Narechania;Joshua C Stein;Doreen Ware

  • From Ukkonen to McCreight and Weiner: A Unifying View of Linear-Time Suffix Tree Construction

    Robert Giegerich;Stefan Kurtz

  • Efficient multiple genome alignment.

    Michael Höhl;Stefan Kurtz;Enno Ohlebusch

  • Efficient implementation of lazy suffix trees

    Robert Giegerich;Stefan Kurtz;Jens Stoye

  • Fast index based algorithms and software for matching position specific scoring matrices

    Michael Beckstette;Robert Homann;Robert Giegerich;Stefan Kurtz

  • The Enhanced Suffix Array and Its Applications to Genome Analysis

    Mohamed Ibrahim Abouelhoda;Stefan Kurtz;Enno Ohlebusch

  • Optimal Exact Strring Matching Based on Suffix Arrays

    Mohamed Ibrahim Abouelhoda;Enno Ohlebusch;Stefan Kurtz

  • An applications-focused review of comparative genomics tools: capabilities, limitations and future challenges.

    Patrick Chain;Stefan Kurtz;Enno Ohlebusch;Tom Slezak

  • Optimal exact string matching based on suffix arrays

    Mohamed Ibrahim Abouelhoda;Enno Ohlebusch;Stefan Kurtz

  • Universal data compression based on the Burrows-Wheeler transformation: theory and practice

    B. Balkenhol;S. Kurtz

Frequent Co-Authors

Robert Giegerich
Robert Giegerich Bielefeld University
Ronald Simon
Ronald Simon Universität Hamburg
Volker Brendel
Volker Brendel Indiana University
Jens Stoye
Jens Stoye Bielefeld University
Malik Alawi
Malik Alawi University Medical Center Hamburg-Eppendorf
Thorsten Schlomm
Thorsten Schlomm Charité - University Medicine Berlin
Markus Graefen
Markus Graefen Universität Hamburg
David Ellinghaus
David Ellinghaus Kiel University
Paul Flicek
Paul Flicek The Jackson Laboratory
Martijn A. Huynen
Martijn A. Huynen Radboud University Medical Center

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