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Immunology
Denmark
2026

D-Index & Metrics

Immunology

D-Index
90
Citations
42145
World Ranking
1058
National Ranking
5

Morten Nielsen publication distribution in Immunology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Immunology in 2026. The highlighted bar marks where Morten Nielsen sits on this spectrum.

62–71 publications: 9 scientists 72–81 publications: 23 scientists 82–91 publications: 40 scientists 92–101 publications: 72 scientists 102–111 publications: 86 scientists 112–121 publications: 108 scientists 122–131 publications: 150 scientists 132–141 publications: 160 scientists 142–151 publications: 159 scientists 152–161 publications: 189 scientists 162–171 publications: 166 scientists 172–181 publications: 181 scientists 182–191 publications: 188 scientists 192–201 publications: 187 scientists 202–211 publications: 178 scientists 212–221 publications: 158 scientists 222–231 publications: 168 scientists 232–241 publications: 159 scientists 242–251 publications: 160 scientists 252–261 publications: 148 scientists 262–271 publications: 116 scientists 272–281 publications: 125 scientists 282–291 publications: 115 scientists 292–301 publications: 111 scientists 302–311 publications: 95 scientists 312–321 publications: 97 scientists 322–331 publications: 97 scientists 332–341 publications: 99 scientists 342–351 publications: 96 scientists 352–361 publications: 68 scientists 362–371 publications: 76 scientists 372–381 publications: 71 scientists 382–391 publications: 71 scientists 392–401 publications: 62 scientists 402–411 publications: 54 scientists 412–421 publications: 41 scientists 422–431 publications: 63 scientists 432–441 publications: 53 scientists 442–451 publications: 31 scientists 452–461 publications: 42 scientists 462–471 publications: 40 scientists 472–481 publications: 29 scientists 482–491 publications: 34 scientists 492–501 publications: 30 scientists 502–511 publications: 23 scientists 512–521 publications: 41 scientists 522–531 publications: 29 scientists 532–541 publications: 28 scientists 542–551 publications: 16 scientists 552–561 publications: 18 scientists 562–571 publications: 18 scientists 572–581 publications: 17 scientists 582–591 publications: 17 scientists 592–601 publications: 17 scientists 602–611 publications: 19 scientists 612–621 publications: 13 scientists 622–631 publications: 12 scientists 632–641 publications: 11 scientists 642–651 publications: 16 scientists 652–661 publications: 9 scientists 662–671 publications: 5 scientists 672–681 publications: 13 scientists 682–691 publications: 12 scientists 692–701 publications: 6 scientists 702–711 publications: 6 scientists 712–721 publications: 9 scientists 722–731 publications: 9 scientists 732–741 publications: 6 scientists 742–751 publications: 11 scientists 752–761 publications: 10 scientists 762–771 publications: 7 scientists 772–781 publications: 12 scientists 782–791 publications: 7 scientists 792–801 publications: 5 scientists 802–806 publications: 1 scientists 807+ publications: 100 scientists
62 publications 807+

This scientist: 515 publications — 90th percentile

90% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 807 publications or more.

Morten Nielsen D-index placement in Immunology in 2026

The chart shows the D-index (discipline H-index) distribution of Immunology scientists ranked by Research.com in 2026. The highlighted bar marks where Morten Nielsen sits on this spectrum.

40–41 D-Index: 28 scientists 42–43 D-Index: 91 scientists 44–45 D-Index: 157 scientists 46–47 D-Index: 192 scientists 48–49 D-Index: 175 scientists 50–51 D-Index: 188 scientists 52–53 D-Index: 177 scientists 54–55 D-Index: 155 scientists 56–57 D-Index: 194 scientists 58–59 D-Index: 201 scientists 60–61 D-Index: 197 scientists 62–63 D-Index: 201 scientists 64–65 D-Index: 173 scientists 66–67 D-Index: 167 scientists 68–69 D-Index: 172 scientists 70–71 D-Index: 199 scientists 72–73 D-Index: 184 scientists 74–75 D-Index: 133 scientists 76–77 D-Index: 162 scientists 78–79 D-Index: 127 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 125 scientists 84–85 D-Index: 121 scientists 86–87 D-Index: 102 scientists 88–89 D-Index: 96 scientists 90–91 D-Index: 75 scientists 92–93 D-Index: 72 scientists 94–95 D-Index: 74 scientists 96–97 D-Index: 66 scientists 98–99 D-Index: 68 scientists 100–101 D-Index: 49 scientists 102–103 D-Index: 60 scientists 104–105 D-Index: 54 scientists 106–107 D-Index: 36 scientists 108–109 D-Index: 23 scientists 110–111 D-Index: 52 scientists 112–113 D-Index: 41 scientists 114–115 D-Index: 34 scientists 116–117 D-Index: 25 scientists 118–119 D-Index: 28 scientists 120–121 D-Index: 11 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 27 scientists 126–127 D-Index: 19 scientists 128–129 D-Index: 20 scientists 130–131 D-Index: 16 scientists 132–133 D-Index: 17 scientists 134–135 D-Index: 14 scientists 136–137 D-Index: 15 scientists 138–139 D-Index: 9 scientists 140–141 D-Index: 11 scientists 142–143 D-Index: 9 scientists 144–145 D-Index: 9 scientists 146–147 D-Index: 7 scientists 148–149 D-Index: 7 scientists 150–151 D-Index: 7 scientists 152–153 D-Index: 5 scientists 154–155 D-Index: 8 scientists 156 D-Index: 6 scientists 157+ D-Index: 96 scientists
40 D-Index 157+

This scientist: 90 D-Index — 79th percentile

79% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 157 D-Index or more.

Research.com Recognitions

  • 2026 - Research.com Immunology in Denmark Leader Award
  • 2025 - Research.com Immunology in Denmark Leader Award
  • 2023 - Research.com Immunology in Denmark Leader Award
  • 2022 - Research.com Immunology in Denmark Leader Award

Overview

Morten Nielsen is affiliated with the Technical University of Denmark in Denmark and has contributed extensively to the fields of Medicine, as well as Biochemistry, Genetics and Molecular Biology. Their research spans various subfields including Molecular Biology, Public Health, Environmental and Occupational Health, Radiology, Nuclear Medicine and Imaging, Infectious Diseases, and Immunology.

The scientist's work addresses topics such as Monoclonal and Polyclonal Antibodies Research, Malaria Research and Control, SARS-CoV-2 and COVID-19 Research, Glycosylation and Glycoproteins Research, Mosquito-borne diseases and control, Complement system in diseases, and vaccines and immunoinformatics approaches.

Key recent publications include:

  • Capsid-like particles decorated with the SARS-CoV-2 receptor-binding domain elicit strong virus neutralization activity, 2021, Nature Communications
  • The C-terminal tail of α-synuclein protects against aggregate replication but is critical for oligomerization, 2022, Communications Biology
  • Afucosylated Plasmodium falciparum-specific IgG is induced by infection but not by subunit vaccination, 2021, Nature Communications
  • Cryo-EM reveals the architecture of placental malaria VAR2CSA and provides molecular insight into chondroitin sulfate binding, 2021, Nature Communications
  • Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria, 2021, eLife

They have collaborated frequently with Ali Salanti, Adam F. Sander, Thor G. Theander, Robert Dagil, and Willem A. de Jongh.

Publication venues regularly featuring their work include Nature Communications, Vaccines, Frontiers in Immunology, bioRxiv (Cold Spring Harbor Laboratory), and Communications Biology.

Best Publications

  • NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data.

    Birkir Reynisson;Bruno Alvarez;Sinu Paul;Bjoern Peters;Bjoern Peters

  • Robust T Cell Immunity in Convalescent Individuals with Asymptomatic or Mild COVID-19.

    Takuya Sekine;André Perez-Potti;Olga Rivera-Ballesteros;Kristoffer Strålin

  • BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes

    Martin Closter Jespersen;Bjoern Peters;Morten Nielsen;Paolo Marcatili

  • Improved method for predicting linear B-cell epitopes.

    Jens Erik Pontoppidan Larsen;Ole Lund;Morten Nielsen

  • NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

    Vanessa Isabell Jurtz;Sinu Paul;Massimo Andreatta;Paolo Marcatili

  • Reliable prediction of T-cell epitopes using neural networks with novel sequence representations

    Morten Nielsen;Claus Lundegaard;Peder Worning;Sanne Lise Lauemøller

  • Gapped sequence alignment using artificial neural networks: application to the MHC class I system.

    Massimo Andreatta;Morten Nielsen

  • Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction

    Mette V Larsen;Claus Lundegaard;Kasper Lamberth;Soren Buus

  • NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11

    Claus Lundegaard;Kasper Lamberth;Mikkel Harndahl;Søren Buus

  • NetMHCpan, a method for MHC class I binding prediction beyond humans

    Ilka Hoof;Bjoern Peters;John Sidney;Lasse Eggers Pedersen

  • Improved methods for predicting peptide binding affinity to MHC class II molecules

    Kamilla Kjærgaard Jensen;Massimo Andreatta;Paolo Marcatili;Søren Buus

  • NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence.

    Morten A. Nielsen;Claus Lundegaard;Thomas H. Blicher;Kasper Lamberth

  • Prediction of residues in discontinuous B-cell epitopes using protein 3D structures

    Pernille Haste Andersen;Morten Nielsen;Ole Lund

  • Peptide binding predictions for HLA DR, DP and DQ molecules

    Peng Wang;John Sidney;Yohan Kim;Alessandro Sette

  • A generic method for assignment of reliability scores applied to solvent accessibility predictions

    Bent Petersen;Thomas Nordahl Petersen;Pernille Andersen;Pernille Andersen;Morten Nielsen

  • Reliable B cell epitope predictions: impacts of method development and improved benchmarking.

    Jens Vindahl Kringelum;Claus Lundegaard;Ole Lund;Morten Nielsen

  • Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

    Morten Nielsen;Claus Lundegaard;Ole Lund

  • NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction.

    Morten Nielsen;Ole Lund

  • NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning

    Michael Schantz Klausen;Martin Closter Jespersen;Henrik Nielsen;Kamilla Kjærgaard Jensen

  • The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage

    Morten Nielsen;Claus Lundegaard;Ole Lund;Can Keşmir;Can Keşmir

  • NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets.

    Morten Nielsen;Massimo Andreatta

Frequent Co-Authors

Ole Lund
Ole Lund Danish National Genome Center
Søren Buus
Søren Buus University of Copenhagen
Bjoern Peters
Bjoern Peters La Jolla Institute For Allergy & Immunology
Alessandro Sette
Alessandro Sette La Jolla Institute For Allergy & Immunology
Charlotte Bay Hasager
Charlotte Bay Hasager Technical University of Denmark
Søren Brunak
Søren Brunak University of Copenhagen
Jason A. Greenbaum
Jason A. Greenbaum La Jolla Institute For Allergy & Immunology
William H. Hildebrand
William H. Hildebrand University of Oklahoma Health Sciences Center
Ali Salanti
Ali Salanti Copenhagen University Hospital
Thor G. Theander
Thor G. Theander University of Copenhagen

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