World's Best Scientists 2026 revealed!
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Computer Science
Spain
2025

D-Index & Metrics

Computer Science

D-Index
44
Citations
10112
World Ranking
7471
National Ranking
97

Natasa Przulj 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 Natasa Przulj 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 89 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

Natasa Przulj 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 Natasa Przulj sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Computer Science in Spain Leader Award
  • 2022 - Research.com Computer Science in Spain Leader Award
  • 2017 - Member of Academia Europaea

Overview

Natasa Przulj is affiliated with the Institució Catalana de Recerca i Estudis Avançats in Spain. Their research primarily contributes to the field of Biochemistry, Genetics and Molecular Biology, with a focus on Molecular Biology, Computational Theory and Mathematics, Neurology, Infectious Diseases, and Genetics.

The scientist's work encompasses a range of topics including Bioinformatics and Genomic Networks, Gene expression and cancer classification, Computational Drug Discovery Methods, Gene Regulatory Network Analysis, Genetics, Bioinformatics, and Biomedical Research, Parkinson's Disease Mechanisms and Treatments, and Biomedical Text Mining and Ontologies.

Recent publications by Natasa Przulj include:

  • Current and future directions in network biology, 2024, Bioinformatics Advances
  • Unveiling new disease, pathway, and gene associations via multi-scale neural network, 2020, PLoS ONE
  • Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing, 2024, Nucleic Acids Research
  • Multi-omics integration of scRNA-seq time series data predicts new intervention points for Parkinson's disease, 2024, Scientific Reports
  • Chromatin network markers of leukemia, 2020, Bioinformatics

Frequent co-authors with whom Natasa Przulj has collaborated include Noël Malod-Dognin, Alexandros Xenos, Gaia Ceddia, Sam F. L. Windels, and Katarina Mihajlović.

The scientist has published extensively in several venues, with a notable presence in:

  • Bioinformatics
  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • Bioinformatics Advances
  • PLoS ONE

Natasa Przulj's research covers areas related to the analysis and modeling of genomic networks, computational methods for drug discovery, and the application of multi-omics data integration to understand complex diseases like Parkinson's disease.

Among the recognitions received, Natasa Przulj became a Member of Academia Europaea in 2017.

Best Publications

  • A global genetic interaction network maps a wiring diagram of cellular function

    Michael Costanzo;Benjamin VanderSluis;Elizabeth N. Koch;Anastasia Baryshnikova

  • Evidence for Network Evolution in an Arabidopsis Interactome Map

    Matija Dreze;Anne-Ruxandra Carvunis;Benoit Charloteaux

  • High-throughput mapping of a dynamic signaling network in mammalian cells.

    Miriam Barrios-Rodiles;Kevin R. Brown;Barish Ozdamar;Barish Ozdamar;Rohit Bose;Rohit Bose

  • Protein complex prediction via cost-based clustering

    A. D. King;N. Pržulj;I. Jurisica

  • Functional topology in a network of protein interactions

    N. Pržulj;D.A. Wigle;I. Jurisica

  • Integrative network alignment reveals large regions of global network similarity in yeast and human

    Oleksii Kuchaiev;Nataša Pržulj

  • Methods for biological data integration: perspectives and challenges

    Vladimir Gligorijević;Nataša Pržulj

  • Optimal network alignment with graphlet degree vectors.

    Tijana Milenković;Tijana Milenković;Weng Leong Ng;Wayne Hayes;Wayne Hayes;Nataša Pržulj

  • Revealing the Hidden Language of Complex Networks

    Ömer Nebil Yaveroğlu;Noël Malod-Dognin;Darren Davis;Zoran Levnajic

  • Integrative methods for analyzing big data in precision medicine

    Vladimir Gligorijević;Noël Malod-Dognin;Nataša Pržulj

  • Geometric De-noising of Protein-Protein Interaction Networks

    Oleksii Kuchaiev;Marija Rasajski;Marija Rasajski;Desmond J. Higham;Natasa Przulj

  • Efficient estimation of graphlet frequency distributions in protein--protein interaction networks

    N. Pržulj;D. G. Corneil;I. Jurisica

  • Characterization of the proteasome interaction network using a QTAX-based tag-team strategy and protein interaction network analysis

    Cortnie Guerrero;Tijana Milenkovic;Natasa Przulj;Peter Kaiser

  • GraphCrunch: A tool for large network analyses

    Tijana Milenković;Jason Lai;Nataša Pržulj

  • L-GRAAL: Lagrangian graphlet-based network aligner.

    Noël Malod-Dognin;Nataša Pržulj

  • Network analytics in the age of big data

    Nataša Pržulj;Noël Malod-Dognin

  • Fitting a geometric graph to a protein–protein interaction network

    Desmond J. Higham;Marija Rašajski;Nataša Pržulj

  • Not all scale free networks are Born equal: the role of the seed graph in PPI network emulation

    Fereydoun Hormozdiari;Petra Berenbrink;Nataša Pržulj;Cenk Sahinalp

  • Dominating biological networks.

    Tijana Milenković;Vesna Memišević;Anthony Bonato;Nataša Pržulj

  • Modeling Interactome: Scale-Free or Geometric?

    Natasa Przulj;Derek G. Corneil;Igor Jurisica

  • Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data

    Tijana Milenković;Vesna Memišević;Anand K. Ganesan;Nataša Pržulj

  • Uncovering Biological Network Function via Graphlet Degree Signatures

    Tijana Milenkovic;Natasa Przulj

  • Proper evaluation of alignment-free network comparison methods

    Ömer Nebil Yaveroğlu;Tijana Milenković;Nataša Pržulj

  • Geometric evolutionary dynamics of protein interaction networks.

    Natasa Przulj;Oleksii Kuchaiev;Aleksandar Stevanovic;Wayne B. Hayes

  • A framework for FPGA acceleration of large graph problems: Graphlet counting case study

    Brahim Betkaoui;David B. Thomas;Wayne Luk;Natasa Przulj

  • Optimized null model for protein structure networks.

    Tijana Milenković;Ioannis Filippis;Michael Lappe;Nataša Pržulj

  • Bridging the gaps in systems biology.

    Marija Cvijovic;Joachim Almquist;Jonas Hagmar;Stefan Hohmann

  • Complementarity of network and sequence information in homologous proteins.

    Vesna Memisevic;Tijana Milenkovic;Natasa Przulj

  • An integrative approach to modeling biological networks.

    Vesna Memisevic;Tijana Milenkovic;Natasa Przulj

  • Anti-nicastrin monoclonal antibodies elicit pleiotropic anti-tumour pharmacological effects in invasive breast cancer cells

    Aleksandra Filipović;Ylenia Lombardo;Monica Fronato;Joel Abrahams

  • ergm.graphlets: A Package for ERG Modeling Based on Graphlet Statistics

    Ömer Nebil Yaveroğlu;Sean M. Fitzhugh;Maciej Kurant;Athina Markopoulou

  • Learning the structure of protein-protein interaction networks.

    Oleksii Kuchaiev;Natasa Przulj

Frequent Co-Authors

Anne J. Ridley
Anne J. Ridley University of Bristol
Jeffery L. Dangl
Jeffery L. Dangl University of North Carolina at Chapel Hill
Jens Nielsen
Jens Nielsen Chalmers University of Technology
Wayne Luk
Wayne Luk Imperial College London
Doreen Ware
Doreen Ware Cold Spring Harbor Laboratory
David E. Hill
David E. Hill Harvard University
Robert J. Schmitz
Robert J. Schmitz University of Georgia
Frederick P. Roth
Frederick P. Roth Lunenfeld-Tanenbaum Research Institute
Marc Vidal
Marc Vidal Harvard University
Igor Jurisica
Igor Jurisica University Health Network

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