World's Best Scientists 2026 revealed!
Frank Emmert-Streib

Frank Emmert-Streib

D-Index & Metrics

Computer Science

D-Index
47
Citations
8784
World Ranking
6503
National Ranking
45

Frank Emmert-Streib 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 Frank Emmert-Streib 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: 292 publications — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Frank Emmert-Streib 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 Frank Emmert-Streib 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

Frank Emmert-Streib is affiliated with Tampere University in Finland. Their research primarily spans the field of computer science, with a focus on several subfields including artificial intelligence, molecular biology, computational theory and mathematics, geometry and topology, and statistical and nonlinear physics.

Their work addresses a diverse range of topics, including:

  • Graph theory and applications
  • Topic modeling
  • Complex network analysis techniques
  • Bioinformatics and genomic networks
  • Computational drug discovery methods
  • Explainable artificial intelligence (XAI)
  • Machine learning in healthcare

Frank Emmert-Streib has contributed multiple papers to peer-reviewed journals. Some of the recent notable publications include:

  • "An Introductory Review of Deep Learning for Prediction Models With Big Data" (2020), published in Frontiers in Artificial Intelligence
  • "Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence" (2021), published in Information Fusion
  • "Named Entity Recognition and Relation Detection for Biomedical Information Extraction" (2020), published in Frontiers in Cell and Developmental Biology
  • "Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges" (2024), published in AI
  • "Robustness of differential gene expression analysis of RNA-seq" (2021), published in Computational and Structural Biotechnology Journal

Their frequent coauthors, some with significant collaborative counts, include:

  • Matthias Dehmer
  • Olli Yli-Harja
  • Shailesh Tripathi
  • Modjtaba Ghorbani
  • Zhen Yang

Frank Emmert-Streib has published extensively in several scientific journals, demonstrating a recurring presence in these venues:

  • Frontiers in Artificial Intelligence
  • Information Sciences
  • Applied Mathematics and Computation
  • IEEE Access
  • Symmetry

Beyond journal articles, their contributions include book publications. Notably, they have authored a book titled Cancer systems biology published by Frontiers Media in 2022.

Best Publications

  • An Introductory Review of Deep Learning for Prediction Models With Big Data

    Frank Emmert-Streib;Zhen Yang;Han Feng;Shailesh Tripathi

  • Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks

    Frank Emmert-Streib;Matthias Dehmer;Benjamin Haibe-Kains

  • A review of connectivity map and computational approaches in pharmacogenomics.

    Aliyu Musa;Laleh Soltan Ghoraie;Shu-Dong Zhang;Galina V. Glazko

  • Fifty years of graph matching, network alignment and network comparison

    Frank Emmert-Streib;Matthias Dehmer;Yongtang Shi

  • Inferring the conservative causal core of gene regulatory networks

    Gökmen Altay;Frank Emmert-Streib

  • Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence

    Andreas Holzinger;Andreas Holzinger;Matthias Dehmer;Frank Emmert-Streib;Rita Cucchiara

  • High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

    Frank Emmert-Streib;Matthias Dehmer

  • Harnessing naturally randomized transcription to infer regulatory relationships among genes.

    Lin S Chen;Frank Emmert-Streib;John D Storey

  • Named Entity Recognition and Relation Detection for Biomedical Information Extraction

    Nadeesha Perera;Matthias Dehmer;Frank Emmert-Streib

  • Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data

    Frank Emmert-Streib;Galina V. Glazko;Gökmen Altay;Gökmen Altay;Ricardo de Matos Simoes

  • Networks for systems biology: conceptual connection of data and function

    Frank Emmert-Streib;M. Dehmer

  • Bagging Statistical Network Inference from Large-Scale Gene Expression Data

    Ricardo de Matos Simoes;Frank Emmert-Streib

  • Unite and conquer

    Galina V. Glazko;Frank Emmert-Streib

  • Pathway Analysis of Expression Data: Deciphering Functional Building Blocks of Complex Diseases

    Frank Emmert-Streib;Galina V. Glazko

  • Revealing differences in gene network inference algorithms on the network level by ensemble methods

    Gökmen Altay;Frank Emmert-Streib

  • On Entropy-Based Molecular Descriptors: Statistical Analysis of Real and Synthetic Chemical Structures

    Matthias Dehmer;Kurt Varmuza;Stephan Borgert;Frank Emmert-Streib

  • Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets

    Yasir Rahmatallah;Frank Emmert-Streib;Galina V. Glazko

  • Analysis of Microarray Data: A Network-Based Approach

    Frank Emmert-Streib;Matthias Dehmer

  • Analysis of Complex Networks: From Biology to Linguistics

    Matthias Dehmer;Frank Emmert-Streib

  • A review of connectivity map and computational approaches in pharmacogenomics

    Unknown

  • The chronic fatigue syndrome: a comparative pathway analysis.

    Frank Emmert-Streib

  • Information Theory and Statistical Learning

    Frank Emmert-Streib;Matthias Dehmer

Frequent Co-Authors

Matthias Dehmer
Matthias Dehmer University of Miami
Zengqiang Chen
Zengqiang Chen Nankai University
Benjamin Haibe-Kains
Benjamin Haibe-Kains Princess Margaret Cancer Centre
Manuel Salto-Tellez
Manuel Salto-Tellez Queen's University Belfast
Andreas Holzinger
Andreas Holzinger BOKU University
Max Mühlhäuser
Max Mühlhäuser Technical University of Darmstadt
Kevin M. Prise
Kevin M. Prise Queen's University Belfast
Gianluca Bontempi
Gianluca Bontempi Université Libre de Bruxelles
John-Dylan Haynes
John-Dylan Haynes Charité - University Medicine Berlin
John Quackenbush
John Quackenbush Harvard University

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