Wouter Boomsma

3.6k total citations
49 papers, 1.7k citations indexed

About

Wouter Boomsma is a scholar working on Molecular Biology, Materials Chemistry and Spectroscopy. According to data from OpenAlex, Wouter Boomsma has authored 49 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Molecular Biology, 17 papers in Materials Chemistry and 8 papers in Spectroscopy. Recurrent topics in Wouter Boomsma's work include Protein Structure and Dynamics (31 papers), Enzyme Structure and Function (15 papers) and Genomics and Phylogenetic Studies (6 papers). Wouter Boomsma is often cited by papers focused on Protein Structure and Dynamics (31 papers), Enzyme Structure and Function (15 papers) and Genomics and Phylogenetic Studies (6 papers). Wouter Boomsma collaborates with scholars based in Denmark, United Kingdom and United States. Wouter Boomsma's co-authors include Kresten Lindorff‐Larsen, Jesper Ferkinghoff‐Borg, Thomas Hamelryck, Eske Willerslev, Rasmus Nielsen, Kasper Munch, Jes Frellsen, John P. Huelsenbeck, Simon Olsson and Søren Hauberg and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nature Communications.

In The Last Decade

Wouter Boomsma

49 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wouter Boomsma Denmark 24 1.4k 393 192 184 153 49 1.7k
Michael D. Tyka United States 19 1.8k 1.4× 705 1.8× 224 1.2× 115 0.6× 92 0.6× 24 2.6k
Michael E. Wall United States 26 1.4k 1.0× 539 1.4× 162 0.8× 237 1.3× 55 0.4× 71 1.8k
Kingshuk Ghosh United States 26 1.4k 1.0× 560 1.4× 120 0.6× 173 0.9× 72 0.5× 67 2.2k
Sichun Yang United States 25 1.4k 1.0× 611 1.6× 269 1.4× 128 0.7× 47 0.3× 58 1.8k
Leo S. D. Caves United Kingdom 17 2.1k 1.6× 476 1.2× 198 1.0× 199 1.1× 187 1.2× 45 2.9k
Douglas L. Theobald United States 24 1.8k 1.3× 435 1.1× 115 0.6× 365 2.0× 202 1.3× 42 2.7k
Federico Morán Spain 20 1.6k 1.2× 401 1.0× 120 0.6× 386 2.1× 131 0.9× 77 2.7k
Faruck Morcos United States 24 2.3k 1.7× 389 1.0× 131 0.7× 412 2.2× 78 0.5× 66 2.7k
Carlos X. Hernández United States 8 1.6k 1.2× 498 1.3× 249 1.3× 78 0.4× 50 0.3× 12 2.1k
Timothy R. Lezon United States 13 1.3k 0.9× 334 0.8× 141 0.7× 93 0.5× 58 0.4× 24 1.6k

Countries citing papers authored by Wouter Boomsma

Since Specialization
Citations

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

Fields of papers citing papers by Wouter Boomsma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wouter Boomsma

This figure shows the co-authorship network connecting the top 25 collaborators of Wouter Boomsma. A scholar is included among the top collaborators of Wouter Boomsma 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 Wouter Boomsma. Wouter Boomsma 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
1.
Hauberg, Søren, et al.. (2025). Foundation models of protein sequences: A brief overview. Current Opinion in Structural Biology. 91. 103004–103004. 1 indexed citations
2.
Saminathan, Anand, et al.. (2025). Deep learning-assisted analysis of single-particle tracking for automated correlation between diffusion and function. Nature Methods. 22(5). 1091–1100. 5 indexed citations
3.
Bartels, Simon, et al.. (2024). A systematic analysis of regression models for protein engineering. PLoS Computational Biology. 20(5). e1012061–e1012061. 3 indexed citations
4.
Jonsson, Nicolas, et al.. (2024). SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions. Nature Communications. 15(1). 9646–9646. 8 indexed citations
5.
Boomsma, Wouter, et al.. (2023). Phosphorylation of Schizosaccharomyces pombe Dss1 mediates direct binding to the ubiquitin‐ligase Dma1 in vitro. Protein Science. 32(9). e4733–e4733. 1 indexed citations
6.
Detlefsen, Nicki Skafte, Søren Hauberg, & Wouter Boomsma. (2022). Learning meaningful representations of protein sequences. Nature Communications. 13(1). 1914–1914. 85 indexed citations
7.
Bugge, Katrine, Mads Nygaard, Martin Nors Pedersen, et al.. (2020). Orchestration of signaling by structural disorder in class 1 cytokine receptors. Cell Communication and Signaling. 18(1). 132–132. 21 indexed citations
8.
Bugge, Katrine, et al.. (2019). IDDomainSpotter: Compositional bias reveals domains in long disordered protein regions—Insights from transcription factors. Protein Science. 29(1). 169–183. 17 indexed citations
9.
Hendus‐Altenburger, Ruth, Catarina B. Fernandes, Katrine Bugge, et al.. (2019). Random coil chemical shifts for serine, threonine and tyrosine phosphorylation over a broad pH range. Journal of Biomolecular NMR. 73(12). 713–725. 31 indexed citations
10.
Bottaro, Sandro, et al.. (2018). Barnaba: software for analysis of nucleic acid structures and trajectories. RNA. 25(2). 219–231. 61 indexed citations
11.
Boomsma, Wouter & Jes Frellsen. (2017). Spherical convolutions and their application in molecular modelling. IT University Of Copenhagen (IT University of Copenhagen). 30. 3433–3443. 30 indexed citations
12.
Fonseca, Rasmus, et al.. (2017). Driving Structural Transitions in Molecular Simulations Using the Nonequilibrium Candidate Monte Carlo. The Journal of Physical Chemistry B. 122(3). 1195–1204. 5 indexed citations
13.
Safavi‐Hemami, Helena, Qing Li, Albert S. Song, et al.. (2016). Rapid expansion of the protein disulfide isomerase gene family facilitates the folding of venom peptides. Proceedings of the National Academy of Sciences. 113(12). 3227–3232. 42 indexed citations
14.
Tian, Pengfei, Kresten Lindorff‐Larsen, Wouter Boomsma, Mogens H. Jensen, & Daniel E. Otzen. (2016). A Monte Carlo Study of the Early Steps of Functional Amyloid Formation. PLoS ONE. 11(1). e0146096–e0146096. 9 indexed citations
15.
Olsson, Simon, et al.. (2015). Bayesian inference of protein ensembles from SAXS data. Physical Chemistry Chemical Physics. 18(8). 5832–5838. 46 indexed citations
16.
Olsson, Simon, Jes Frellsen, Wouter Boomsma, Kanti V. Mardia, & Thomas Hamelryck. (2013). Inference of Structure Ensembles of Flexible Biomolecules from Sparse, Averaged Data. PLoS ONE. 8(11). e79439–e79439. 42 indexed citations
17.
Borg, Mikael, Sandro Bottaro, Wouter Boomsma, et al.. (2012). An Efficient Null Model for Conformational Fluctuations in Proteins. Structure. 20(6). 1028–1039. 7 indexed citations
18.
Olsson, Simon, Wouter Boomsma, Jes Frellsen, et al.. (2011). Generative probabilistic models extend the scope of inferential structure determination. Journal of Magnetic Resonance. 213(1). 182–186. 14 indexed citations
19.
Hamelryck, Thomas, Mikael Borg, Jonas Paulsen, et al.. (2010). Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized. PLoS ONE. 5(11). e13714–e13714. 52 indexed citations
20.
Boomsma, Wouter & Thomas Hamelryck. (2005). Full cyclic coordinate descent: solving the protein loop closure problem in Cα space. BMC Bioinformatics. 6(1). 159–159. 20 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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