World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
6232
World Ranking
10703
National Ranking
670

Paulo J. G. Lisboa 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 Paulo J. G. Lisboa 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: 261 publications — 65th percentile

65% of scientists in this discipline score the same or lower.

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

Paulo J. G. Lisboa 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 Paulo J. G. Lisboa 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Paulo J. G. Lisboa is affiliated with Liverpool John Moores University in the United Kingdom. Their research spans multiple disciplines including Computer Science, Medicine, and Biochemistry, Genetics, and Molecular Biology. Within these broad fields, Lisboa's work frequently addresses subfields such as Artificial Intelligence, Molecular Biology, Cell Biology, Surgery, and Physical Therapy, Sports Therapy, and Rehabilitation.

Lisboa has a diverse range of research topics, reflecting interdisciplinary interests. Notable areas include Muscle Metabolism and Nutrition, Explainable Artificial Intelligence (XAI), Muscle Physiology and Disorders, Machine Learning and Data Classification, Balance, Gait, and Falls Prevention, Machine Learning in Healthcare, and Bayesian Modeling and Causal Inference.

Among their recent publications are:

  • The coming of age of interpretable and explainable machine learning models, 2023, Neurocomputing
  • A neural network method to predict task- and step-specific ground reaction force magnitudes from trunk accelerations during running activities, 2020, Medical Engineering & Physics
  • Prediction of Balance Perturbations and Falls on Stairs in Older People Using a Biomechanical Profiling Approach: A 12-Month Longitudinal Study, 2020, The Journals of Gerontology Series A
  • Adaptation of rat fast-twitch muscle to endurance activity is underpinned by changes to protein degradation as well as protein synthesis, 2020, The FASEB Journal
  • Explainable inflation forecasts by machine learning models, 2022, Expert Systems with Applications

Lisboa has contributed to book publications as well, including authorship in "Deep Learning in Biology and Medicine" published by WORLD SCIENTIFIC (EUROPE) eBooks in 2021.

Their work is published in a variety of scientific venues, with multiple publications appearing in:

  • PLoS ONE
  • The FASEB Journal
  • ESANN 2021 proceedings
  • Scientific Reports
  • bioRxiv (Cold Spring Harbor Laboratory)

Lisboa frequently collaborates with several researchers, with repeated coauthorships including:

  • Sandra Ortega-Martorell
  • Iván Olier
  • Jatin G. Burniston
  • Ian H. Jarman
  • Connor A. Stead

Best Publications

  • The use of artificial neural networks in decision support in cancer: A systematic review

    Paulo J. Lisboa;Azzam F. G. Taktak

  • A review of evidence of health benefit from artificial neural networks in medical intervention

    P. J. G. Lisboa

  • Translation, rotation, and scale invariant pattern recognition by high-order neural networks and moment classifiers

    S.J. Perantonis;P.J.G. Lisboa

  • Making machine learning models interpretable

    Alfredo Vellido Alcacena;Jose D. Martin Guerrero;Paulo J.G. Lisboa

  • Segmentation of the on-line shopping market using neural networks

    A. Vellido;P.J.G. Lisboa;K. Meehan

  • A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer

    P. J. G. Lisboa;H. Wong;P. Harris;R. Swindell

  • Quantitative characterization and prediction of on-line purchasing behavior: a latent variable approach

    Alfredo Vellido;Paulo J. G. Lisboa;Karon Meehan

  • The value of personalised recommender systems to e-business: a case study

    M. Benjamin Dias;Dominique Locher;Ming Li;Wael El-Deredy

  • Orthogonal search-based rule extraction (OSRE) for trained neural networks: a practical and efficient approach

    T.A. Etchells;P.J.G. Lisboa

  • Artificial Neural Networks in Biomedicine

    P. J. Lisboa;Piotr S. Szczepaniak;J. C. Mason;Emmanuel C. Ifeachor

  • Fuzzy systems in medicine

    Piotr S. Szczepaniak;Paulo J. G. Lisboa;Janusz Kacprzyk

  • Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection.

    Y. Huang;P. J. G. Lisboa;W. El-Deredy

  • Business applications of neural networks : the state-of-the-art of real-world applications

    Paulo J G Lisboa;Alfredo Vellido;Bill Edisbury

  • Gait quality assessment using self-organising artificial neural networks.

    Gabor Barton;Paulo Lisboa;Adrian Lees;Steve Attfield

  • Financial time series prediction using polynomial pipelined neural networks

    Abir Jaafar Hussain;Adam Knowles;Paulo J. G. Lisboa;Wael El-Deredy

  • Grocery shopping recommendations based on basket-sensitive random walk

    Ming Li;Benjamin M. Dias;Ian Jarman;Wael El-Deredy

  • A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients

    Daniele Soria;Jonathan M. Garibaldi;Federico Ambrogi;Andrew R. Green

  • Special Issue on Neural Networks

    P.J.G. Lisboa

  • Data Mining in Cancer Research [Application Notes]

    P.J.G. Lisboa;A. Vellido;R. Tagliaferri;F. Napolitano

  • Partial Logistic Artificial Neural Network for Competing Risks Regularized With Automatic Relevance Determination

    P.J.G. Lisboa;T.A. Etchells;I.H. Jarman;C.T.C. Arsene

  • Complete solution of the local minima in the XOR problem

    P J G Lisboa;S J Perantonis

Frequent Co-Authors

Wael El-Deredy
Wael El-Deredy Valparaiso University
Ian O. Ellis
Ian O. Ellis University of Nottingham
Stavros J. Perantonis
Stavros J. Perantonis Demokritos National Centre for Scientific Research
Jonathan M. Garibaldi
Jonathan M. Garibaldi University of Nottingham
Enrique Romero
Enrique Romero Universitat Politècnica de Catalunya
Andrew R. Green
Andrew R. Green University of Nottingham
Mark A Bellis
Mark A Bellis Liverpool John Moores University
Constantinos N. Maganaris
Constantinos N. Maganaris Liverpool John Moores University
Chris A. Collins
Chris A. Collins Liverpool John Moores University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science opens up a world of opportunities, but it also connects closely with other in-demand fields. For students interested in applying technology to environmental challenges, learning about what can you get with an environmental science degree reveals a variety of impactful roles that blend science, engineering, and data analysis.

If you’re looking to fast-track your career, consider the benefits of an online computer science accelerated program. These options enable motivated learners to earn their degree more quickly and enter the workforce sooner.

Engineering majors also offer flexibility through online learning. Programs like environmental engineer degree online and online degree in mechanical engineering allow students to specialize in fast-growing sectors within engineering, all from the comfort of home.

As technology continues to evolve, earning a related online degree not only broadens your knowledge but also increases your career options in a competitive global market.

Best Scientists Citing Paulo J. G. Lisboa

Trending Scientists

Recently Published Articles