Siegfried Gessulat

4.3k total citations · 1 hit paper
12 papers, 1.1k citations indexed

About

Siegfried Gessulat is a scholar working on Molecular Biology, Spectroscopy and Information Systems and Management. According to data from OpenAlex, Siegfried Gessulat has authored 12 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 10 papers in Spectroscopy and 1 paper in Information Systems and Management. Recurrent topics in Siegfried Gessulat's work include Advanced Proteomics Techniques and Applications (10 papers), Metabolomics and Mass Spectrometry Studies (5 papers) and Genomics and Phylogenetic Studies (4 papers). Siegfried Gessulat is often cited by papers focused on Advanced Proteomics Techniques and Applications (10 papers), Metabolomics and Mass Spectrometry Studies (5 papers) and Genomics and Phylogenetic Studies (4 papers). Siegfried Gessulat collaborates with scholars based in Germany, United States and Belgium. Siegfried Gessulat's co-authors include Mathias Wilhelm, Bernhard Küster, Tobias Schmidt, Patroklos Samaras, Stephan Aiche, Daniel P. Zolg, Bernard Delanghe, Julia Rechenberger, Karsten Schnatbaum and Tobias Knaute and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Nature Methods.

In The Last Decade

Siegfried Gessulat

11 papers receiving 1.1k citations

Hit Papers

align trajectories

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.

Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning

2019 519 citations Siegfried Gessulat, Tobias Schmidt et al. Nature Methods profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Siegfried Gessulat Germany	10	877	579	94	56	50	12	1.1k
Patroklos Samaras Germany	12	879 1.0×	531 0.9×	97 1.0×	50 0.9×	58 1.2×	15	1.1k
Stephan Aiche Germany	8	758 0.9×	452 0.8×	89 0.9×	48 0.9×	39 0.8×	10	1.0k
Eugenia Voytik Germany	9	720 0.8×	579 1.0×	75 0.8×	61 1.1×	43 0.9×	10	1.1k
Lin‐Yang Cheng United States	5	800 0.9×	578 1.0×	86 0.9×	77 1.4×	57 1.1×	7	1.2k
Heiner Koch Germany	9	706 0.8×	451 0.8×	82 0.9×	55 1.0×	73 1.5×	14	1.0k
Susan E. Abbatiello United States	16	883 1.0×	797 1.4×	72 0.8×	50 0.9×	50 1.0×	19	1.2k
Guo Ci Teo United States	12	926 1.1×	594 1.0×	143 1.5×	111 2.0×	72 1.4×	13	1.2k
Stephanie Kaspar‐Schoenefeld Germany	5	621 0.7×	467 0.8×	59 0.6×	49 0.9×	53 1.1×	6	888
Oliver Raether Germany	6	882 1.0×	718 1.2×	58 0.6×	63 1.1×	70 1.4×	8	1.2k
Amol Prakash United States	18	781 0.9×	544 0.9×	84 0.9×	45 0.8×	34 0.7×	29	1.1k

Countries citing papers authored by Siegfried Gessulat

Since Specialization

Citations

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

Fields of papers citing papers by Siegfried Gessulat

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Siegfried Gessulat

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

All Works

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12 of 12 papers shown

1.

Schneider, Markus, Daniel P. Zolg, Patroklos Samaras, et al.. (2025). A Scalable, Web-Based Platform for Proteomics Data Processing, Result Storage and Analysis. Journal of Proteome Research. 24(3). 1241–1249. 1 indexed citations

2.

Frejno, Martin, Johanna Tüshaus, Alexander Hogrebe, et al.. (2025). Unifying the analysis of bottom-up proteomics data with CHIMERYS. Nature Methods. 22(5). 1017–1027. 9 indexed citations

3.

Gabriels, Ralf, Robbin Bouwmeester, Siegfried Gessulat, et al.. (2023). ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics. Journal of Proteome Research. 22(2). 632–636. 14 indexed citations

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.

Verbruggen, Steven, Siegfried Gessulat, Ralf Gabriels, et al.. (2021). Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Molecular & Cellular Proteomics. 20. 100076–100076. 26 indexed citations

6.

Gessulat, Siegfried, Tobias Schmidt, Mathias Wilhelm, & Bernhard Küster. (2021). kusterlab/prosit: v1.1.2. Zenodo (CERN European Organization for Nuclear Research).

7.

Zolg, Daniel P., Siegfried Gessulat, Daniel López‐Ferrer, et al.. (2021). INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results. Rapid Communications in Mass Spectrometry. 39(S1). e9128–e9128. 51 indexed citations

8.

Searle, Brian C., Kristian E. Swearingen, Christopher A. Barnes, et al.. (2020). Generating high quality libraries for DIA MS with empirically corrected peptide predictions. Nature Communications. 11(1). 1548–1548. 166 indexed citations

9.

Verbruggen, Steven, Elvis Ndah, Wim Van Criekinge, et al.. (2019). PROTEOFORMER 2.0: Further Developments in the Ribosome Profiling-assisted Proteogenomic Hunt for New Proteoforms. Molecular & Cellular Proteomics. 18(8). S126–S140. 37 indexed citations

10.

Samaras, Patroklos, Tobias Schmidt, Martin Frejno, et al.. (2019). ProteomicsDB: a multi-omics and multi-organism resource for life science research. Nucleic Acids Research. 48(D1). D1153–D1163. 129 indexed citations

11.

Gessulat, Siegfried, Tobias Schmidt, Daniel P. Zolg, et al.. (2019). Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nature Methods. 16(6). 509–518. 519 indexed citations breakdown →

12.

Schmidt, Tobias, Patroklos Samaras, Martin Frejno, et al.. (2017). ProteomicsDB. Nucleic Acids Research. 46(D1). D1271–D1281. 148 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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