World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
8866
World Ranking
6202
National Ranking
371

Natalio Krasnogor 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 Natalio Krasnogor 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: 234 publications — 58th percentile

58% of scientists in this discipline score the same or lower.

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

Natalio Krasnogor 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 Natalio Krasnogor 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: 48 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

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

Overview

Natalio Krasnogor is affiliated with Newcastle University in the United Kingdom. Their research primarily focuses on Biochemistry, Genetics, and Molecular Biology, with a significant contribution to the subfields of Molecular Biology, Biomedical Engineering, Ecology, Artificial Intelligence, and Genetics.

The scientist's work covers several key topics within biological sciences and biotechnology:

  • Advanced biosensing and bioanalysis techniques
  • CRISPR and Genetic Engineering
  • Bacteriophages and microbial interactions
  • Microbial Metabolic Engineering and Bioproduction
  • RNA Interference and Gene Delivery
  • Gene Regulatory Network Analysis
  • Bacterial Genetics and Biotechnology

Natalio Krasnogor has contributed to numerous scientific articles, with recent publications highlighting a diversity of research interests. Notable papers include:

  • For the sake of the Bioeconomy: define what a Synthetic Biology Chassis is!, 2020, New Biotechnology
  • Linking Engineered Cells to Their Digital Twins: A Version Control System for Strain Engineering, 2020, ACS Synthetic Biology
  • Toward Full-Stack In Silico Synthetic Biology: Integrating Model Specification, Simulation, Verification, and Biological Compilation, 2021, ACS Synthetic Biology
  • Transcriptomic Responses to Coaggregation between Streptococcus gordonii and Streptococcus oralis, 2021, Applied and Environmental Microbiology
  • Cotranscriptional Folding of a Bio-orthogonal Fluorescent Scaffolded RNA Origami, 2020, ACS Synthetic Biology

The scientist frequently publishes in several academic venues, including:

  • ACS Synthetic Biology
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Nature Communications
  • New Biotechnology
  • Applied and Environmental Microbiology

Collaboration plays a notable role in Natalio Krasnogor's research, with frequent co-authors including:

  • Emanuela Torelli
  • Víctor de Lorenzo
  • Ben Shirt-Ediss
  • Jonathan Tellechea-Luzardo
  • Silvia Adriana Navarro

Best Publications

  • A tutorial for competent memetic algorithms: model, taxonomy, and design issues

    N. Krasnogor;J. Smith

  • Real-coded memetic algorithms with crossover hill-climbing

    Manuel Lozano;Francisco Herrera;Natalio Krasnogor;Daniel Molina

  • EnrichNet: network-based gene set enrichment analysis

    Enrico Glaab;Anaïs Baudot;Natalio Krasnogor;Reinhard Schneider

  • Recent advances in memetic algorithms

    William E. Hart;N. Krasnogor;J. E. Smith

  • Studies on the theory and design space of memetic algorithms.

    Krasnogor N

  • Nature‐inspired cooperative strategies for optimization

    Unknown

  • Multimeme Algorithms for Protein Structure Prediction

    Natalio Krasnogor;B. P. Blackburne;Edmund K. Burke;J. D. Hirst

  • A Memetic Algorithm with self-adaptive local search: TSP as a case study

    Natalio Krasnogor;Jim Smith

  • Measuring the similarity of protein structures by means of the universal similarity metric

    N. Krasnogor;D. A. Pelta

  • Protein structure prediction with evolutionary algorithms

    Natalio Krasnogor;William E. Hart;Jim Smith;David A. Pelta

  • Memetic Evolutionary Algorithms

    W. E. Hart;N. Krasnogor;J. E. Smith

  • The imitation game—a computational chemical approach to recognizing life

    Leroy Cronin;Natalio Krasnogor;Benjamin G Davis;Cameron Alexander

  • Using Rule-Based Machine Learning for Candidate Disease Gene Prioritization and Sample Classification of Cancer Gene Expression Data

    Enrico Glaab;Jaume Bacardit;Jonathan M. Garibaldi;Natalio Krasnogor

  • A Study on the use of self-generation'' in memetic algorithms

    Natalio Krasnogor;Steven Gustafson

  • ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

    Enrico Glaab;Jonathan M Garibaldi;Natalio Krasnogor

  • Emergence of profitable search strategies based on a simple inheritance mechanism

    Natalio Krasnogor;Jim Smith

  • Prediction of human population responses to toxic compounds by a collaborative competition.

    Federica Eduati;Lara M Mangravite;Tao Wang;Hao Tang

  • Improving the scalability of rule-based evolutionary learning

    Jaume Bacardit;Edmund K. Burke;Natalio Krasnogor

  • A study on the design issues of Memetic Algorithm

    Q.H. Nguyen;Y.S. Ong;N. Krasnogor

  • Advanced Population Diversity Measures in Genetic Programming

    Edmund K. Burke;Steven M. Gustafson;Graham Kendall;Natalio Krasnogor

  • TopoGSA: network topological gene set analysis

    Enrico Glaab;Anaïs Baudot;Natalio Krasnogor;Alfonso Valencia

  • Bacteria clustering by polymers induces the expression of quorum-sensing-controlled phenotypes

    Leong T. Lui;Xuan Xue;Cheng Sui;Alan Brown

Frequent Co-Authors

Jaume Bacardit
Jaume Bacardit Newcastle University
Jonathan M. Garibaldi
Jonathan M. Garibaldi University of Nottingham
Jonathan D. Hirst
Jonathan D. Hirst University of Nottingham
Edmund K. Burke
Edmund K. Burke Bangor University
Cameron Alexander
Cameron Alexander University of Nottingham
Marcus Kaiser
Marcus Kaiser University of Nottingham
Gabriela Ochoa
Gabriela Ochoa University of Stirling
Miguel Cámara
Miguel Cámara University of Nottingham
Jacek Blazewicz
Jacek Blazewicz Poznań University of Technology
Majlinda Lako
Majlinda Lako Newcastle University

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