Morten Nielsen

422 total papers · 40.1k total citations
277 papers, 25.5k 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 277 papers receiving a total of 25.5k indexed citations (citations by other indexed papers that have themselves been cited), including 217 papers in Molecular Biology, 148 papers in Immunology and 94 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Morten Nielsen's work include vaccines and immunoinformatics approaches (175 papers), Immunotherapy and Immune Responses (111 papers) and Monoclonal and Polyclonal Antibodies Research (93 papers). Morten Nielsen is often cited by papers focused on vaccines and immunoinformatics approaches (175 papers), Immunotherapy and Immune Responses (111 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 25.1k citations

Hit Papers

NetMHCpan-4.1 and NetMHCI... 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 benchmark for papers published in the same subfield and year, 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 Birkir Reynisson, Bruno Alvarez et al. Nucleic Acids Research profile →
  2. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes
    2017 1.1k citations Martin Closter Jespersen, Bjoern Peters et al. Nucleic Acids Research profile →
  3. Improved method for predicting linear B-cell epitopes.
    2006 978 citations Jens Larsen, Ole Lund et al. PubMed profile →
  4. NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data
    2017 875 citations Vanessa Jurtz, Sinu Paul et al. The Journal of Immunology profile →
  5. Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations
    2003 830 citations Morten Nielsen, Claus Lundegaard et al. Protein Science profile →
  6. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction
    2007 712 citations Mette Voldby Larsen, Claus Lundegaard et al. BMC Bioinformatics profile →
  7. Gapped sequence alignment using artificial neural networks: application to the MHC class I system
    2015 709 citations Massimo Andreatta, Morten Nielsen Bioinformatics 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 590 citations Claus Lundegaard, Kasper Lamberth et al. Nucleic Acids Research profile →
  9. NetMHCpan, a method for MHC class I binding prediction beyond humans
    2008 541 citations Ilka Hoof, Bjoern Peters et al. Immunogenetics profile →
  10. Improved methods for predicting peptide binding affinity to MHC class II molecules
    2018 523 citations Kamilla Kjærgaard Jensen, Massimo Andreatta et al. Immunology profile →
  11. Peptide binding predictions for HLA DR, DP and DQ molecules
    2010 519 citations Peng Wang, John Sidney et al. BMC Bioinformatics profile →
  12. A generic method for assignment of reliability scores applied to solvent accessibility predictions
    2009 505 citations Bent Petersen, Thomas Nordahl Petersen et al. BMC Structural Biology profile →
  13. Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking
    2012 497 citations Jens Vindahl Kringelum, Claus Lundegaard et al. PLoS Computational Biology profile →
  14. NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning
    2019 382 citations Michael Schantz Klausen, Martin Closter Jespersen et al. Proteins Structure Function and Bioinformatics profile →
  15. Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data
    2020 271 citations Birkir Reynisson, Carolina Barra et al. Journal of Proteome Research profile →
  16. IEDB-AR: immune epitope database—analysis resource in 2019
    2019 260 citations Sandeep Kumar Dhanda, Swapnil Mahajan 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 148 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 119 citations Magnus Haraldson Høie, Bjoern Peters et al. Protein Science profile →
  19. The receiver operating characteristic curve accurately assesses imbalanced datasets
    2024 52 citations Jason Greenbaum, Hannah Carter et al. Patterns profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Morten Nielsen 19.1k 12.0k 7.3k 3.6k 3.3k 277 25.5k
Bjoern Peters 18.5k 1.0× 14.0k 1.2× 6.4k 0.9× 8.8k 2.4× 3.0k 0.9× 353 32.0k
John Sidney 14.6k 0.8× 19.4k 1.6× 5.3k 0.7× 6.6k 1.8× 3.3k 1.0× 387 32.9k
Richard D. Cummings 22.0k 1.2× 12.4k 1.0× 3.8k 0.5× 1.6k 0.4× 2.4k 0.7× 511 33.0k
Jay A. Berzofsky 9.3k 0.5× 17.8k 1.5× 3.5k 0.5× 2.6k 0.7× 5.0k 1.5× 434 29.2k
James C. Paulson 26.1k 1.4× 13.5k 1.1× 6.7k 0.9× 4.4k 1.2× 2.2k 0.7× 393 42.5k
Martin F. Bachmann 8.3k 0.4× 18.0k 1.5× 2.5k 0.3× 4.1k 1.1× 3.6k 1.1× 370 31.2k
Pamela J. Björkman 9.6k 0.5× 14.6k 1.2× 7.3k 1.0× 3.2k 0.9× 1.8k 0.5× 245 28.9k
Søren Buus 9.4k 0.5× 10.1k 0.8× 4.6k 0.6× 1.8k 0.5× 1.9k 0.6× 295 17.4k
César Milstein 18.4k 1.0× 13.9k 1.2× 17.7k 2.4× 1.0k 0.3× 3.4k 1.0× 202 35.9k
Georges Köhler 11.1k 0.6× 10.1k 0.8× 11.0k 1.5× 1.2k 0.3× 3.1k 0.9× 97 25.2k

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

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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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