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Gianluca Pollastri

Gianluca Pollastri

University College Dublin
Ireland

D-Index & Metrics

Computer Science

D-Index
38
Citations
7658
World Ranking
10090
National Ranking
34

Gianluca Pollastri 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 Gianluca Pollastri 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: 87 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.

Gianluca Pollastri 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 Gianluca Pollastri 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

Gianluca Pollastri is affiliated with University College Dublin in Ireland. Their research primarily spans the fields of Biochemistry, Genetics and Molecular Biology, with a strong emphasis on Molecular Biology. Additional subfields include Computational Theory and Mathematics, Biophysics, Artificial Intelligence, and Materials Chemistry.

The scientist's work focuses on several main topics, reflecting interdisciplinary approaches within life sciences and computational methods. These topics include:

  • Machine Learning in Bioinformatics
  • Protein Structure and Dynamics
  • Genomics and Phylogenetic Studies
  • RNA and protein synthesis mechanisms
  • Computational Drug Discovery Methods
  • Genetics, Bioinformatics, and Biomedical Research
  • Cell Image Analysis Techniques

Pollastri has published extensively in multiple scientific venues, with repeated contributions to the following journals and platforms:

  • Computational and Structural Biotechnology Journal
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Bioinformatics
  • Nature Methods
  • Proteins Structure Function and Bioinformatics

Notable recent publications include the following papers:

  • "Deep learning methods in protein structure prediction," 2020, Computational and Structural Biotechnology Journal
  • "DOME: recommendations for supervised machine learning validation in biology," 2021, Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano)
  • "SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks," 2020, Bioinformatics
  • "Protein subcellular localization prediction tools," 2024, Computational and Structural Biotechnology Journal
  • "Prediction of polyproline II secondary structure propensity in proteins," 2020, Royal Society Open Science

Frequent collaborators in Gianluca Pollastri's research include:

  • Di Meng
  • Mirko Torrisi
  • Ian Walsh
  • Dmytro Fishman
  • Tiina Titma

Best Publications

  • Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles.

    Gianluca Pollastri;Darisz Przybylski;Burkhard Rost;Pierre Baldi

  • Exploiting the past and the future in protein secondary structure prediction.

    Pierre Baldi;Søren Brunak;Paolo Frasconi;Giovanni Soda

  • Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules

    Alessandro Lusci;Gianluca Pollastri;Pierre Baldi

  • Porter: a new, accurate server for protein secondary structure prediction

    Gianluca Pollastri;Aoife Mclysaght

  • Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity

    Catherine Mooney;Niall J. Haslam;Gianluca Pollastri;Denis C. Shields

  • Prediction of coordination number and relative solvent accessibility in proteins.

    Gianluca Pollastri;Pierre Baldi;Pietro Fariselli;Rita Casadio

  • Deep learning methods in protein structure prediction.

    Mirko Torrisi;Gianluca Pollastri;Quan Le

  • The principled design of large-scale recursive neural network architectures--dag-rnns and the protein structure prediction problem

    Pierre Baldi;Gianluca Pollastri

  • A neural network approach to ordinal regression

    Jianlin Cheng;Zheng Wang;G. Pollastri

  • DOME: recommendations for supervised machine learning validation in biology

    Ian Walsh;Dmytro Fishman;Dario Garcia-Gasulla;Tiina Titma

  • Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners.

    Gianluca Pollastri;Pierre Baldi

  • CPPpred: prediction of cell penetrating peptides.

    Thérèse A. Holton;Gianluca Pollastri;Denis C. Shields;Catherine Mooney

  • Spritz: a server for the prediction of intrinsically disordered regions in protein sequences using kernel machines.

    Alessandro Vullo;Oscar Bortolami;Gianluca Pollastri;Silvio C. E. Tosatto

  • Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information.

    Gianluca Pollastri;Alberto J M Martin;Catherine Mooney;Alessandro Vullo

  • Porter, PaleAle 4.0: high-accuracy prediction of protein secondary structure and relative solvent accessibility

    Claudio Mirabello;Gianluca Pollastri

  • Bidirectional dynamics for protein secondary structure prediction

    Pierre Baldi;Søren Brunak;Paolo Frasconi;Gianluca Pollastri

  • A two-stage approach for improved prediction of residue contact maps

    Alessandro Vullo;Ian Walsh;Gianluca Pollastri

  • Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins.

    Davide Baù;Alberto J. M. Martin;Catherine Mooney;Alessandro Vullo

  • CSpritz: accurate prediction of protein disorder segments with annotation for homology, secondary structure and linear motifs

    Ian Walsh;Alberto J. M. Martin;Tomàs Di Domenico;Alessandro Vullo

  • Prediction of Short Linear Protein Binding Regions

    Catherine Mooney;Gianluca Pollastri;Denis C. Shields;Niall J. Haslam

  • Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks

    Ian Walsh;Davide Baù;Alberto J M Martin;Catherine Mooney

Frequent Co-Authors

Pierre Baldi
Pierre Baldi University of California, Irvine
Denis C. Shields
Denis C. Shields University College Dublin
Paolo Frasconi
Paolo Frasconi University of Florence
Søren Brunak
Søren Brunak University of Copenhagen
Silvio C. E. Tosatto
Silvio C. E. Tosatto University of Padua
Rita Casadio
Rita Casadio University of Bologna
Jianlin Cheng
Jianlin Cheng University of Missouri
Jeremy C. Simpson
Jeremy C. Simpson University College Dublin
Norman E. Davey
Norman E. Davey Institute of Cancer Research
Burkhard Rost
Burkhard Rost Technical University of Munich

External Links

Personal website of Gianluca Pollastri Google Scholar page

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