Veit Schwämmle

4.4k total citations
99 papers, 2.8k citations indexed

About

Veit Schwämmle is a scholar working on Molecular Biology, Spectroscopy and Statistical and Nonlinear Physics. According to data from OpenAlex, Veit Schwämmle has authored 99 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Molecular Biology, 37 papers in Spectroscopy and 14 papers in Statistical and Nonlinear Physics. Recurrent topics in Veit Schwämmle's work include Advanced Proteomics Techniques and Applications (35 papers), Metabolomics and Mass Spectrometry Studies (18 papers) and Mass Spectrometry Techniques and Applications (16 papers). Veit Schwämmle is often cited by papers focused on Advanced Proteomics Techniques and Applications (35 papers), Metabolomics and Mass Spectrometry Studies (18 papers) and Mass Spectrometry Techniques and Applications (16 papers). Veit Schwämmle collaborates with scholars based in Denmark, Brazil and Germany. Veit Schwämmle's co-authors include Ole N. Jensen, Hans J. Herrmann, Ileana R. León, Fernando Nobre, Evaldo M. F. Curado, Richard R. Sprenger, Orencio Durán, Simone Sidoli, Adelina Rogowska-Wrzesińska and Martin R. Larsen and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Veit Schwämmle

94 papers receiving 2.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Veit Schwämmle Denmark 32 1.3k 645 415 359 281 99 2.8k
Anders Johansen Sweden 60 399 0.3× 767 1.2× 110 0.3× 743 2.1× 361 1.3× 230 12.3k
Ofer Biham Israel 36 1.0k 0.8× 261 0.4× 72 0.2× 865 2.4× 13 0.0× 127 4.9k
P. Hubert France 34 1.3k 1.0× 43 0.1× 40 0.1× 82 0.2× 172 0.6× 142 3.8k
Jens Ledet Jensen Denmark 31 5.6k 4.3× 71 0.1× 63 0.2× 66 0.2× 55 0.2× 113 9.7k
Leonard A. Smith United States 46 1.8k 1.4× 45 0.1× 19 0.0× 589 1.6× 24 0.1× 201 7.5k
Sergei Maslov United States 38 2.3k 1.8× 52 0.1× 23 0.1× 1.8k 5.0× 26 0.1× 116 6.8k
Koh Takeuchi Japan 34 2.2k 1.7× 462 0.7× 7 0.0× 20 0.1× 144 0.5× 207 5.1k
David S. Whitley United States 25 276 0.2× 130 0.2× 75 0.2× 179 0.5× 8 0.0× 95 2.3k
R. Leféver Belgium 32 850 0.7× 30 0.0× 15 0.0× 1.9k 5.3× 38 0.1× 64 5.2k
Jack W. Scannell United Kingdom 20 1.2k 0.9× 36 0.1× 12 0.0× 216 0.6× 61 0.2× 28 3.7k

Countries citing papers authored by Veit Schwämmle

Since Specialization
Citations

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

Fields of papers citing papers by Veit Schwämmle

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Veit Schwämmle

This figure shows the co-authorship network connecting the top 25 collaborators of Veit Schwämmle. A scholar is included among the top collaborators of Veit Schwämmle 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 Veit Schwämmle. Veit Schwämmle 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.
Jensen, Ole N., et al.. (2025). How to Deal With Internal Fragment Ions?. Molecular & Cellular Proteomics. 24(5). 100896–100896.
2.
Havelund, Jesper F., et al.. (2024). MetaboLink: a web application for streamlined processing and analysis of large-scale untargeted metabolomics data. Bioinformatics. 40(7). 2 indexed citations
3.
Bouyssié, David, Salvador Capella-Gutiérrez, José M. Fernández, et al.. (2023). WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows. Journal of Proteome Research. 23(1). 418–429.
4.
Neely, Benjamin A., Viktoria Dorfer, Lennart Martens, et al.. (2023). Toward an Integrated Machine Learning Model of a Proteomics Experiment. Journal of Proteome Research. 22(3). 681–696. 35 indexed citations
5.
Schwämmle, Veit, et al.. (2022). VIQoR: a web service for visually supervised protein inference and protein quantification. Bioinformatics. 38(10). 2757–2764. 1 indexed citations
6.
Krawczyk, Konrad, et al.. (2022). Variability analysis of LC-MS experimental factors and their impact on machine learning. GigaScience. 12. 7 indexed citations
7.
Schwämmle, Veit, et al.. (2021). MS2AI: automated repurposing of public peptide LC-MS data for machine learning applications. Bioinformatics. 38(3). 875–877. 6 indexed citations
8.
Bittremieux, Wout, David Bouyssié, Viktoria Dorfer, et al.. (2021). The European Bioinformatics Community for Mass Spectrometry (EuBIC‐MS): an open community for bioinformatics training and research. Rapid Communications in Mass Spectrometry. 39(S1). e9087–e9087. 3 indexed citations
9.
Kirsch, Rebecca, Ole N. Jensen, & Veit Schwämmle. (2020). Visualization of the dynamics of histone modifications and their crosstalk using PTM-CrossTalkMapper. Methods. 184. 78–85. 16 indexed citations
10.
Shliaha, Pavel V., Vladimir Gorshkov, Sergey I. Kovalchuk, et al.. (2020). Middle-Down Proteomic Analyses with Ion Mobility Separations of Endogenous Isomeric Proteoforms. Analytical Chemistry. 92(3). 2364–2368. 27 indexed citations
11.
Nadzieja, Marcin, Lene H. Madsen, Christoph A. Bücherl, et al.. (2019). A Lotus japonicus cytoplasmic kinase connects Nod factor perception by the NFR5 LysM receptor to nodulation. Proceedings of the National Academy of Sciences. 116(28). 14339–14348. 28 indexed citations
12.
Palmblad, Magnus, et al.. (2019). One Thousand and One Software for Proteomics: Tales of the Toolmakers of Science. Journal of Proteome Research. 18(10). 3580–3585. 15 indexed citations
13.
Willems, Sander, David Bouyssié, Dieter Deforce, et al.. (2018). Proceedings of the EuBIC developer's meeting 2018. Journal of Proteomics. 187. 25–27. 3 indexed citations
14.
LeDuc, Richard D., Veit Schwämmle, Michael R. Shortreed, et al.. (2018). ProForma: A Standard Proteoform Notation. Journal of Proteome Research. 17(3). 1321–1325. 28 indexed citations
15.
Willems, Sander, David Bouyssié, Marie Locard‐Paulet, et al.. (2017). Proceedings of the EuBIC Winter School 2017. Journal of Proteomics. 161. 78–80. 6 indexed citations
16.
Edwards, Alistair V.G., Veit Schwämmle, & Martin R. Larsen. (2014). Neuronal process structure and growth proteins are targets of heavy PTM regulation during brain development. Journal of Proteomics. 101. 77–87. 8 indexed citations
17.
Williamson, James C., Peter Scheipers, Veit Schwämmle, et al.. (2013). A proteomics approach to the identification of biomarkers for psoriasis utilising keratome biopsy. Journal of Proteomics. 94. 176–185. 31 indexed citations
18.
Manteca, Ángel, Jesús Sánchez, Hye Ryung Jung, Veit Schwämmle, & Ole N. Jensen. (2010). Quantitative Proteomics Analysis of Streptomyces coelicolor Development Demonstrates That Onset of Secondary Metabolism Coincides with Hypha Differentiation. Molecular & Cellular Proteomics. 9(7). 1423–1436. 39 indexed citations
19.
Schwämmle, Veit, Marta C. González, Andrés Moreira, José S. Andrade, & H. J. Herrmann. (2007). The spread of opinions in a model with different topologies. arXiv (Cornell University). 2 indexed citations
20.
Durán, Orencio, Veit Schwämmle, Pedro G. Lind, & Hans J. Herrmann. (2007). How barchan dunes distribute over deserts. arXiv (Cornell University). 1 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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