World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
5689
World Ranking
12529
National Ranking
43

Miguel Rocha 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 Miguel Rocha 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: 347 publications — 81st percentile

81% of scientists in this discipline score the same or lower.

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

Miguel Rocha 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 Miguel Rocha 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Miguel Rocha is affiliated with the University of Minho in Portugal and has an extensive research profile in the fields of biochemistry, genetics, and molecular biology. Their work spans multiple subfields including molecular biology, biomedical engineering, computational theory and mathematics, surgery, and materials chemistry.

Their research covers a range of topics, with significant contributions in microbial metabolic engineering and bioproduction, bioinformatics and genomic networks, computational drug discovery methods, gene regulatory network analysis, biofuel production and bioconversion, machine learning in materials science, and plant biochemistry and biosynthesis.

Recent publications by Miguel Rocha include:

  • Generative Deep Learning for Targeted Compound Design, 2021, Journal of Chemical Information and Modeling
  • Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses, 2021, Molecular Systems Biology
  • UPIMAPI, reCOGnizer and KEGGCharter: Bioinformatics tools for functional annotation and visualization of (meta)-omics datasets, 2022, Computational and Structural Biotechnology Journal
  • merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models, 2022, Nucleic Acids Research
  • Evaluating molecular representations in machine learning models for drug response prediction and interpretability, 2022, Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics

Miguel Rocha has frequently collaborated with co-authors including Óscar Dias, Vítor Pereira, João Capela, Fernando Cruz, and Ana Marta Sequeira.

The scientist's work has been published in prominent venues, with multiple publications in:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Zenodo (CERN European Organization for Nuclear Research)
  • PLoS Computational Biology
  • Computational and Structural Biotechnology Journal
  • Molecular Systems Biology

In addition to journal articles, Miguel Rocha has contributed to book publications with Springer International Publishing and Springer Nature. Titles include:

  • Practical Applications of Computational Biology and Bioinformatics, 16th International Conference (PACBB 2022), 2022
  • Practical Applications of Computational Biology and Bioinformatics, 17th International Conference (PACBB 2023), 2023
  • Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020), 2020

Best Publications

  • OptFlux: an open-source software platform for in silico metabolic engineering

    Isabel Rocha;Paulo Maia;Pedro Evangelista;Paulo Vilaça

  • Particle swarms for feedforward neural network training

    R. Mendes;P. Cortez;M. Rocha;J. Neves

  • Integrating Sustainable Development into Existing Management Systems

    Miguel Rocha;Cory Searcy;Stanislav Karapetrovic

  • Modeling formalisms in Systems Biology

    Daniel Machado;Rafael S Costa;Miguel Rocha;Eugénio C Ferreira

  • Multi-scale Internet traffic forecasting using neural networks and time series methods

    Paulo Cortez;Miguel Rio;Miguel Rocha;Pedro Sousa

  • Deep learning for drug response prediction in cancer

    Delora Baptista;Pedro G Ferreira;Miguel Rocha

  • Generative deep learning for targeted compound design

    Tiago Sousa;João Correia;Vítor Pereira;Miguel Rocha

  • Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data

    Justin Guinney;Tao Wang;Teemu D Laajala;Teemu D Laajala;Kimberly Kanigel Winner

  • Reconstructing genome-scale metabolic models with merlin

    Oscar Dias;Miguel Rocha;Eugénio C. Ferreira;Isabel Rocha

  • Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living

    Sigeru Omatu;Miguel P. Rocha;José Bravo;Florentino Fernández

  • Evolution of neural networks for classification and regression

    Miguel Rocha;Paulo Cortez;José Neves

  • Internet Traffic Forecasting using Neural Networks

    P. Cortez;M. Rio;M. Rocha;P. Sousa

  • Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses.

    Aurelien Dugourd;Christoph Kuppe;Christoph Kuppe;Marco Sciacovelli;Enio Gjerga;Enio Gjerga

  • Preventing premature convergence to local optima in genetic algorithms via random offspring generation

    Miguel Rocha;José Neves

  • Internet Traffic Forecasting using Neural Networks

    Unknown

  • Natural computation meta-heuristics for the in silico optimization of microbial strains

    Miguel Rocha;Paulo Maia;Rui Mendes;José P Pinto

  • Bridging the layers: towards integration of signal transduction, regulation and metabolism into mathematical models.

    Emanuel Gonçalves;Joachim Bucher;Anke Ryll;Jens Niklas

  • Evolving Time Series Forecasting ARMA Models

    Paulo Cortez;Miguel Rocha;José Neves

  • A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering.

    Osvaldo D. Kim;Miguel Rocha;Paulo Maia

  • Optimization of fed-batch fermentation processes with bio-inspired algorithms

    Miguel Rocha;Rui Mendes;Orlando Rocha;Isabel Rocha

  • 13C-based metabolic flux analysis

    Rafael Carreira;S. Carneiro;S. G. Villas-Bôas;I. Rocha

Frequent Co-Authors

Eugénio C. Ferreira
Eugénio C. Ferreira University of Minho
Paulo Cortez
Paulo Cortez University of Minho
Marcelo Maraschin
Marcelo Maraschin Universidade Federal de Santa Catarina
José Neves
José Neves University of Porto
Florentino Fdez-Riverola
Florentino Fdez-Riverola Universidade de Vigo
Julio Saez-Rodriguez
Julio Saez-Rodriguez Heidelberg University
Ross Overbeek
Ross Overbeek Argonne National Laboratory
Nuno Sousa
Nuno Sousa Centro Universitario Max Planck
Kiran Raosaheb Patil
Kiran Raosaheb Patil European Bioinformatics Institute
Jens Nielsen
Jens Nielsen Chalmers University of Technology

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