Morten Nielsen

40.7k total citations · 20 hit papers
278 papers, 25.8k citations indexed

About

Morten Nielsen is a scholar working on Molecular Biology, Immunology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Morten Nielsen has authored 278 papers receiving a total of 25.8k indexed citations (citations by other indexed papers that have themselves been cited), including 218 papers in Molecular Biology, 149 papers in Immunology and 94 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Morten Nielsen's work include vaccines and immunoinformatics approaches (176 papers), Immunotherapy and Immune Responses (112 papers) and Monoclonal and Polyclonal Antibodies Research (93 papers). Morten Nielsen is often cited by papers focused on vaccines and immunoinformatics approaches (176 papers), Immunotherapy and Immune Responses (112 papers) and Monoclonal and Polyclonal Antibodies Research (93 papers). Morten Nielsen collaborates with scholars based in Denmark, Argentina and United States. Morten Nielsen's co-authors include Ole Lund, Claus Lundegaard, Bjoern Peters, Søren Buus, Massimo Andreatta, Paolo Marcatili, Alessandro Sette, Kasper Lamberth, Sinu Paul and Martin Closter Jespersen and has published in prestigious journals such as Cell, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Morten Nielsen

272 papers receiving 25.3k citations

Hit Papers

NetMHCpan-4.1 and Net... 2003 2026 2010 2018 2020 2017 2006 2017 2003 250 500 750 1000
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. 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
    2020 1.2k citations Bjoern Peters, Morten Nielsen et al. Nucleic Acids Research profile →
  2. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes
    2017 1.1k citations Bjoern Peters, Morten Nielsen et al. Nucleic Acids Research profile →
  3. Improved method for predicting linear B-cell epitopes.
    2006 981 citations Ole Lund, Morten Nielsen et al. profile →
  4. NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data
    2017 888 citations Massimo Andreatta, Paolo Marcatili et al. The Journal of Immunology profile →
  5. Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations
    2003 832 citations Morten Nielsen, Claus Lundegaard et al. profile →
  6. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction
    2007 721 citations Claus Lundegaard, Søren Buus et al. profile →
  7. Gapped sequence alignment using artificial neural networks: application to the MHC class I system
    2015 716 citations Massimo Andreatta, Morten Nielsen profile →
  8. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11
    2008 591 citations Claus Lundegaard, Søren Buus et al. Nucleic Acids Research profile →
  9. NetMHCpan, a method for MHC class I binding prediction beyond humans
    2008 546 citations Bjoern Peters, John Sidney et al. Immunogenetics profile →
  10. Improved methods for predicting peptide binding affinity to MHC class II molecules
    2018 530 citations Massimo Andreatta, Paolo Marcatili et al. profile →
  11. Peptide binding predictions for HLA DR, DP and DQ molecules
    2010 526 citations John Sidney, Alessandro Sette et al. profile →
  12. A generic method for assignment of reliability scores applied to solvent accessibility predictions
    2009 507 citations Bent Petersen, Morten Nielsen et al. profile →
  13. Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking
    2012 502 citations Claus Lundegaard, Ole Lund et al. profile →
  14. NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning
    2019 388 citations Henrik Nielsen, Ole Winther et al. profile →
  15. Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data
    2020 272 citations Carolina Barra, Saghar Kaabinejadian et al. profile →
  16. IEDB-AR: immune epitope database—analysis resource in 2019
    2019 264 citations Massimo Andreatta, Paolo Marcatili et al. Nucleic Acids Research profile →
  17. NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning
    2022 164 citations Magnus Haraldson Høie, Bent Petersen et al. Nucleic Acids Research profile →
  18. BepiPred ‐3.0: Improved B‐cell epitope prediction using protein language models
    2022 129 citations Magnus Haraldson Høie, Bjoern Peters et al. profile →
  19. The receiver operating characteristic curve accurately assesses imbalanced datasets
    2024 59 citations Morten Nielsen, Bjoern Peters et al. profile →
  20. DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations
    2024 50 citations Magnus Haraldson Høie, Ole Winther et al. Frontiers in Immunology profile →

Peers

Morten Nielsen
Comparison fields: 5 of 179
  • Molecular Biology 19.3k
  • Immunology 12.1k
  • Radiology, Nuclear Medicine and Imaging 7.3k
  • Infectious Diseases 3.7k
  • Oncology 3.3k
Replace Bjoern Peters with:
Bjoern Peters United States
Richard D. Cummings United States
John Sidney United States
James C. Paulson United States
Jay A. Berzofsky United States
Pamela J. Björkman United States
Søren Buus Denmark
John P. Moore United States
Michel C. Nussenzweig United States
Dimiter S. Dimitrov United States
Bjoern Peters United States View profile →
Citations per field, relative to Morten Nielsen
Morten Nielsen · 1×
Citations per year, relative to Morten Nielsen
Morten Nielsen · 1×

Countries citing papers authored by Morten Nielsen

Since Specialization
Citations

This map shows the geographic impact of Morten Nielsen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Morten Nielsen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Morten Nielsen more than expected).

Fields of papers citing papers by Morten Nielsen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Morten Nielsen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Morten Nielsen. The network helps show where Morten Nielsen may publish in the future.

Co-authorship network of co-authors of Morten Nielsen

This figure shows the co-authorship network connecting the top 25 collaborators of Morten Nielsen. A scholar is included among the top collaborators of Morten Nielsen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Morten Nielsen. Morten Nielsen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1
Identification of T-cell epitopes of the intracellular parasite Babesia bovis by immunopeptidomic analysis of BoLA-II presented peptides
2025 ·Vaccine ·(unknown), Robert Parker, Annalisa Nicastri, (unknown), María José Gravisaco, (unknown), (unknown), (unknown), Morten Nielsen, Nicola Ternette, Silvina Elizabeth Wilkowsky
0
2
Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integration
2024 ·eLife ·(unknown), Morten Nielsen
1
3
An analysis pipeline for understanding 6-thioguanine effects on a mouse tumour genome
2024 ·Cancer Immunology Immunotherapy ·Patricio Yankilevich, (unknown), (unknown), Kjeld Schmiegelow, Daniela De Zio, Morten Nielsen
1
4
AntiFold: improved structure-based antibody design using inverse folding
2024 ·Bioinformatics Advances ·Magnus Haraldson Høie, Alissa M. Hummer, Tobias Hegelund Olsen, (unknown), Morten Nielsen, Charlotte M. Deane
10
5
Lessons learned from the IMMREP23 TCR-epitope prediction challenge
2024 ·SHILAP Revista de lepidopterología ·Morten Nielsen, Anne Eugster, (unknown), Manisha Goel, (unknown), (unknown), (unknown), María Rodríguez Martínez, (unknown), Victor Greiff, Thierry Mora, Aleksandra M. Walczak, Giancarlo Croce, (unknown), David Gfeller, Pieter Meysman, Justin Barton
14
6
Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome
2023 ·Communications Biology ·(unknown), Saghar Kaabinejadian, (unknown), Bjoern Peters, Carolina Barra, Loren Gragert, William H. Hildebrand, Morten Nielsen
29
7
Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report
2023 ·SHILAP Revista de lepidopterología ·Pieter Meysman, Justin Barton, Barbara Bravi, Liel Cohen-Lavi, (unknown), (unknown), Alessandro Montemurro, Morten Nielsen, Thierry Mora, (unknown), (unknown), María Rodríguez Martínez, Jorge Fernández-de-Cossio-Díaz, (unknown), (unknown), Anna Weber, (unknown), Anne Eugster, Virag Sharma
51
8
Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integration
2023 ·eLife ·(unknown), Morten Nielsen
19
9
Improved T cell receptor antigen pairing through data-driven filtering of sequencing information from single cells
2023 ·eLife ·Helle Rus Povlsen, Amalie Kai Bentzen, (unknown), Leon Eyrich Jessen, Sine Reker Hadrup, Morten Nielsen
14
10
The structure of songbird MHC class I reveals antigen binding that is flexible at the N-terminus and static at the C-terminus
2023 ·Frontiers in Immunology ·(unknown), (unknown), Søren Buus, Morten Nielsen, Kajsa Paulsson, Karin Lindkvist‐Petersson, Helena Westerdahl
2
11
NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning breakdown →
2022 ·Nucleic Acids Research ·Magnus Haraldson Høie, (unknown), Bent Petersen, Morten Nielsen, Ole Winther, Henrik Nielsen, (unknown), Paolo Marcatili
164
12
Thiopurine 6TG treatment increases tumor immunogenicity and response to immune checkpoint blockade
2022 ·OncoImmunology ·(unknown), (unknown), Patricio Yankilevich, Luca Di Leo, Francesca M. Bosisio, Alex Frías, Corine Bertolotto, Jacob Nersting, Ole Thastrup, Søren Buus, Allan Randrup Thomsen, Morten Nielsen, Kristoffer Staal Rohrberg, Kjeld Schmiegelow, Daniela De Zio
6
13
SARS-CoV-2 genome-wide T cell epitope mapping reveals immunodominance and substantial CD8 + T cell activation in COVID-19 patients
2021 ·Science Immunology ·Sunil Kumar Saini, Ditte Stampe Hersby, Tripti Tamhane, Helle Rus Povlsen, (unknown), Morten Nielsen, Anne Ortved Gang, Sine Reker Hadrup
138
14
Neoantigen-reactive CD8+ T cells affect clinical outcome of adoptive transfer with tumor-infiltrating lymphocytes in melanoma
2021 ·UCL Discovery (University College London) ·(unknown), (unknown), Siri Tvingsholm, Annie Borch, Arianna Draghi, Michael D. Crowther, (unknown), (unknown), (unknown), Anne-Mette Bjerregaard, Amalie Kai Bentzen, Andrea Marion Marquard, Zoltán Szállási, Nicholas McGranahan, Rikke Andersen, Morten Nielsen, Göran Jönsson, Marco Donia, Inge Marie Svane, Sine Reker Hadrup
77
15
In Silico Guided Discovery of Novel Class I and II Trypanosoma cruzi Epitopes Recognized by T Cells from Chagas’ Disease Patients
2020 ·The Journal of Immunology ·(unknown), Natalia Juiz, Andrea Ziblat, (unknown), (unknown), (unknown), Marisa Fernández, (unknown), (unknown), Michael Wittig, André Franke, Morten Nielsen, Karina A. Gómez
7
16
NetH2pan: A Computational Tool to Guide MHC Peptide Prediction on Murine Tumors
2018 ·Cancer Immunology Research ·(unknown), Massimo Andreatta, Wilfried Bardet, (unknown), (unknown), Kenneth W. Jackson, Alana L. Welm, Morten Nielsen, William H. Hildebrand
15
17
Citrullination only infrequently impacts peptide binding to HLA class II MHC
2017 ·PLoS ONE ·John Sidney, Stéphane Bécart, Mimi Zhou, Karen E. Duffy, (unknown), Erin Moore, (unknown), Tadimeti S. Rao, Navin Rao, Morten Nielsen, Bjoern Peters, Alessandro Sette
38
18
The Length Distribution of Class I–Restricted T Cell Epitopes Is Determined by Both Peptide Supply and MHC Allele–Specific Binding Preference
2016 ·The Journal of Immunology ·Thomas Trolle, Curtis McMurtrey, John Sidney, Wilfried Bardet, (unknown), Thomas Kaever, Alessandro Sette, William H. Hildebrand, Morten Nielsen, Bjoern Peters
151
19
NetMHCcons: a consensus method for the major histocompatibility complex class I predictions
2011 ·Immunogenetics ·Edita Karosiene, Claus Lundegaard, Ole Lund, Morten Nielsen
270
20
Pinocytic activity of rabbit alveolar macrophages in vitro.
1978 ·Munich Personal RePEc Archive (Ludwig Maximilian University of Munich) ·Jeffrey B. Kaplan, Morten Nielsen
24

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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