Birkir Reynisson

3.0k total citations · 2 hit papers
10 papers, 1.6k citations indexed

About

Birkir Reynisson is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Immunology. According to data from OpenAlex, Birkir Reynisson has authored 10 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 6 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Immunology. Recurrent topics in Birkir Reynisson's work include vaccines and immunoinformatics approaches (8 papers), Monoclonal and Polyclonal Antibodies Research (6 papers) and Immunotherapy and Immune Responses (2 papers). Birkir Reynisson is often cited by papers focused on vaccines and immunoinformatics approaches (8 papers), Monoclonal and Polyclonal Antibodies Research (6 papers) and Immunotherapy and Immune Responses (2 papers). Birkir Reynisson collaborates with scholars based in Denmark, Argentina and United Kingdom. Birkir Reynisson's co-authors include Morten Nielsen, Bjoern Peters, Bruno Alvarez, Sinu Paul, Carolina Barra, Saghar Kaabinejadian, William H. Hildebrand, Nicola Ternette, Søren Buus and Massimo Andreatta and has published in prestigious journals such as Nucleic Acids Research, The Journal of Immunology and Frontiers in Immunology.

In The Last Decade

Birkir Reynisson

9 papers receiving 1.6k citations

Hit Papers

NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions o... 2020 2026 2022 2024 2020 2020 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 Birkir Reynisson, Bruno Alvarez et al. Nucleic Acids Research profile →
  2. Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data
    2020 272 citations Birkir Reynisson, Carolina Barra et al. Journal of Proteome Research profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Birkir Reynisson Denmark 7 1.2k 842 472 313 279 10 1.6k
Bruno Alvarez Denmark 6 1.2k 1.0× 833 1.0× 466 1.0× 257 0.8× 293 1.1× 6 1.6k
Ilka Hoof Denmark 20 1.6k 1.3× 997 1.2× 432 0.9× 196 0.6× 229 0.8× 37 2.4k
Michael Rasmussen Denmark 20 1.1k 0.9× 815 1.0× 348 0.7× 214 0.7× 223 0.8× 40 1.7k
Oleksandr Kalyuzhniy United States 17 1.3k 1.1× 837 1.0× 596 1.3× 508 1.6× 251 0.9× 26 2.4k
Sanne Lise Lauemøller Denmark 10 1.1k 0.9× 816 1.0× 417 0.9× 163 0.5× 171 0.6× 11 1.5k
Swapnil Mahajan United States 13 2.0k 1.6× 1.0k 1.2× 701 1.5× 585 1.9× 243 0.9× 26 2.6k
Mikkel Harndahl Denmark 21 1.7k 1.4× 1.7k 2.1× 626 1.3× 236 0.8× 439 1.6× 40 2.6k
Jutta Bachmann Germany 10 1.5k 1.2× 1.6k 1.9× 558 1.2× 156 0.5× 332 1.2× 13 2.5k
Frances Terry United States 22 704 0.6× 537 0.6× 352 0.7× 260 0.8× 95 0.3× 49 1.2k
Leonard Moise United States 33 1.5k 1.2× 1.1k 1.3× 774 1.6× 453 1.4× 172 0.6× 99 2.7k

Countries citing papers authored by Birkir Reynisson

Since Specialization
Citations

This map shows the geographic impact of Birkir Reynisson'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 Birkir Reynisson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Birkir Reynisson more than expected).

Fields of papers citing papers by Birkir Reynisson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Birkir Reynisson. 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 Birkir Reynisson. The network helps show where Birkir Reynisson may publish in the future.

Co-authorship network of co-authors of Birkir Reynisson

This figure shows the co-authorship network connecting the top 25 collaborators of Birkir Reynisson. A scholar is included among the top collaborators of Birkir Reynisson 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 Birkir Reynisson. Birkir Reynisson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
1.
Hreggviðsson, Guðmundur Ó., Snædís H. Björnsdóttir, Ólafur H. Friðjónsson, et al.. (2024). A genome-scale metabolic reconstruction provides insight into the metabolism of the thermophilic bacterium Rhodothermus marinus. FEMS Microbiology Ecology. 101(1).
2.
Reynisson, Birkir, et al.. (2024). New light on the HLA-DR immunopeptidomic landscape. Journal of Leukocyte Biology. 115(5). 913–925. 6 indexed citations
3.
Connelley, Timothy, Annalisa Nicastri, Tara A. Sheldrake, et al.. (2022). Immunopeptidomic Analysis of BoLA-I and BoLA-DR Presented Peptides from Theileria parva Infected Cells. Vaccines. 10(11). 1907–1907. 6 indexed citations
4.
Fisch, Andressa, Birkir Reynisson, Lindert Benedictus, et al.. (2021). Integral Use of Immunopeptidomics and Immunoinformatics for the Characterization of Antigen Presentation and Rational Identification of BoLA-DR–Presented Peptides and Epitopes. The Journal of Immunology. 206(10). 2489–2497. 25 indexed citations
5.
Reynisson, Birkir, Carolina Barra, Saghar Kaabinejadian, et al.. (2020). Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data. Journal of Proteome Research. 19(6). 2304–2315. 272 indexed citations breakdown →
6.
Barra, Carolina, Birkir Reynisson, Heidi S. Schultz, et al.. (2020). Improved prediction of HLA antigen presentation hotspots: Applications for immunogenicity risk assessment of therapeutic proteins. Immunology. 162(2). 208–219. 12 indexed citations
7.
Barra, Carolina, Chloé Ackaert, Birkir Reynisson, et al.. (2020). Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction. Frontiers in Immunology. 11. 1304–1304. 21 indexed citations
8.
Reynisson, Birkir, Bruno Alvarez, Sinu Paul, Bjoern Peters, & Morten Nielsen. (2020). 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. Nucleic Acids Research. 48(W1). W449–W454. 1192 indexed citations breakdown →
9.
Alvarez, Bruno, Birkir Reynisson, Carolina Barra, et al.. (2019). NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions. Molecular & Cellular Proteomics. 18(12). 2459–2477. 85 indexed citations
10.
Reynisson, Birkir, M. J. Brunger, M. Hoshino, et al.. (2014). Negative ion formation through dissociative electron attachment to the group IV tetrabromides: Carbon tetrabromide, silicon tetrabromide and germanium tetrabromide. International Journal of Mass Spectrometry. 365-366. 275–280. 6 indexed citations

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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