World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
4893
World Ranking
12190
National Ranking
39

Sara Silva 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 Sara Silva 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: 134 publications — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Sara Silva 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 Sara Silva 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Sara Silva is affiliated with the University of Lisbon in Portugal and has contributed extensively to the field of Computer Science, with a particular focus on Artificial Intelligence. Their research spans several subfields including Molecular Biology, Plant Science, Pulmonary and Respiratory Medicine, and Global and Planetary Change.

Their most recent publications include:

  • A Machine Learning Approach to Predict Air Quality in California, 2020, Complexity
  • Evolving knowledge graph similarity for supervised learning in complex biomedical domains, 2020, BMC Bioinformatics
  • A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI, 2023, Cancers
  • An Intelligent Intrusion Detection System for 5G-Enabled Internet of Vehicles, 2023, Electronics
  • Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in Arabidopsis thaliana, 2021, Applied Sciences

Their collaborative network includes frequent co-authors such as:

  • Leonardo Vanneschi (38 publications)
  • Nuno M. Rodrigues (18 publications)
  • João E. Batista (17 publications)
  • Rita T. Sousa (12 publications)
  • Cátia Pesquita (12 publications)

Frequently chosen venues for publishing include:

  • Zenodo (CERN European Organization for Nuclear Research) with 7 publications
  • arXiv (Cornell University) with 7 publications
  • Climate Services with 3 publications
  • Computers in Biology and Medicine with 3 publications
  • Preprints.org with 3 publications

Sara Silva has also published a book titled Lectures on Intelligent Systems in 2023 under Springer Science+Business Media, which has accumulated 25 citations.

The main topics of their work cover:

  • Evolutionary Algorithms and Applications
  • Metaheuristic Optimization Algorithms Research
  • Bioinformatics and Genomic Networks
  • Biomedical Text Mining and Ontologies
  • Horticultural and Viticultural Research
  • Viral Infectious Diseases and Gene Expression in Insects
  • Machine Learning and Data Classification

Best Publications

  • GPLAB A Genetic Programming Toolbox for MATLAB

    Sara Silva

  • A Machine Learning Approach to Predict Air Quality in California

    Mauro Castelli;Fabiana Martins Clemente;Aleš Popovič;Aleš Popovič;Sara Silva

  • Comparative phylogenetic analyses uncover the ancient roots of Indo-European folktales

    Sara Graça da Silva;Jamshid J. Tehrani

  • A survey of semantic methods in genetic programming

    Leonardo Vanneschi;Mauro Castelli;Sara Silva

  • Combination of Cell-Penetrating Peptides with Nanoparticles for Therapeutic Application: A Review

    Sara Silva;António J Almeida;Nuno Vale

  • Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories

    Sara Silva;Ernesto Costa

  • Prediction of high performance concrete strength using Genetic Programming with geometric semantic genetic operators

    Mauro Castelli;Mauro Castelli;Leonardo Vanneschi;Leonardo Vanneschi;Sara Silva

  • Measuring bloat, overfitting and functional complexity in genetic programming

    Leonardo Vanneschi;Mauro Castelli;Sara Silva

  • A C++ framework for geometric semantic genetic programming

    Mauro Castelli;Sara Silva;Leonardo Vanneschi

  • Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

    Sara Silva;Anna I. Esparcia-Alcázar

  • A new implementation of geometric semantic GP and its application to problems in pharmacokinetics

    Leonardo Vanneschi;Mauro Castelli;Luca Manzoni;Sara Silva

  • Dynamic Maximum Tree Depth

    Sara Silva;Jonas S. Almeida;Jonas S. Almeida

  • Geometric Semantic Genetic Programming for Real Life Applications

    Leonardo Vanneschi;Leonardo Vanneschi;Leonardo Vanneschi;Sara Silva;Sara Silva;Mauro Castelli;Mauro Castelli;Luca Manzoni

  • Dynamic maximum tree depth: a simple technique for avoiding bloat in tree-based GP

    Sara Silva;Jonas Almeida

  • Balancing learning and overfitting in genetic programming with interleaved sampling of training data

    Ivo Gonçalves;Sara Silva

  • Random sampling technique for overfitting control in genetic programming

    Ivo Gonçalves;Sara Silva;Joana B. Melo;João M. B. Carreiras

  • Operator equalisation for bloat free genetic programming and a survey of bloat control methods

    Sara Silva;Stephen Dignum;Leonardo Vanneschi

  • Dynamic Limits for Bloat Control

    Sara Silva;Ernesto Costa

  • Prediction of the Unified Parkinson’s Disease Rating Scale assessment using a genetic programming system with geometric semantic genetic operators

    Mauro Castelli;Leonardo Vanneschi;Sara Silva

  • Burned area estimations derived from Landsat ETM+ and OLI data: Comparing Genetic Programming with Maximum Likelihood and Classification and Regression Trees

    Ana I.R. Cabral;Sara Silva;Sara Silva;Pedro C. Silva;Leonardo Vanneschi

  • neat Genetic Programming

    Leonardo Trujillo;Luis Muñoz;Edgar Galván-López;Sara Silva

Frequent Co-Authors

Leonardo Vanneschi
Leonardo Vanneschi Universidade Nova de Lisboa
Mauro Castelli
Mauro Castelli Universidade Nova de Lisboa
Carlos M. Fonseca
Carlos M. Fonseca University of Coimbra
Aleš Popovič
Aleš Popovič NEOMA Business School
Maria Rosário Bronze
Maria Rosário Bronze University of Lisbon
Lee Spector
Lee Spector Hampshire College
Jason H. Moore
Jason H. Moore University of Pennsylvania
Stefania Sarno
Stefania Sarno University of Bologna
Mait Metspalu
Mait Metspalu University of Tartu
Hermínia de Lencastre
Hermínia de Lencastre Rockefeller University

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