Sven Eyckerman

3.7k total citations
70 papers, 1.8k citations indexed

About

Sven Eyckerman is a scholar working on Molecular Biology, Cell Biology and Oncology. According to data from OpenAlex, Sven Eyckerman has authored 70 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 15 papers in Cell Biology and 14 papers in Oncology. Recurrent topics in Sven Eyckerman's work include Biotin and Related Studies (12 papers), Bioinformatics and Genomic Networks (10 papers) and Cytokine Signaling Pathways and Interactions (10 papers). Sven Eyckerman is often cited by papers focused on Biotin and Related Studies (12 papers), Bioinformatics and Genomic Networks (10 papers) and Cytokine Signaling Pathways and Interactions (10 papers). Sven Eyckerman collaborates with scholars based in Belgium, United Kingdom and Germany. Sven Eyckerman's co-authors include Jan Tavernier, Joël Vandekerckhove, Annick Verhee, Irma Lemmens, Kris Gevaert, Delphine Lavens, Frank Peelman, Lennart Zabeau, D. Broekaert and Delphine De Sutter and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Sven Eyckerman

68 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sven Eyckerman Belgium 23 981 331 322 310 285 70 1.8k
Frank Peelman Belgium 32 1.2k 1.2× 484 1.5× 639 2.0× 470 1.5× 221 0.8× 71 3.1k
Stéphanie Kermorgant United Kingdom 24 1.1k 1.1× 486 1.5× 838 2.6× 507 1.6× 403 1.4× 40 2.9k
Richard B. Jones United States 22 1.7k 1.7× 364 1.1× 74 0.2× 370 1.2× 233 0.8× 60 2.7k
Julia A. Yaglom United States 24 1.8k 1.9× 215 0.6× 87 0.3× 247 0.8× 626 2.2× 31 2.4k
Kristoffer Rigbolt Denmark 28 1.8k 1.8× 774 2.3× 80 0.2× 297 1.0× 478 1.7× 52 2.8k
Kenneth Wu United States 25 2.2k 2.2× 653 2.0× 60 0.2× 738 2.4× 424 1.5× 47 2.9k
Nicolas Gévry Canada 25 1.9k 1.9× 161 0.5× 92 0.3× 562 1.8× 98 0.3× 52 2.6k
Alessia David United Kingdom 24 1.2k 1.2× 83 0.3× 182 0.6× 88 0.3× 105 0.4× 50 2.1k
Kazuhiro Kitada Japan 21 688 0.7× 595 1.8× 57 0.2× 183 0.6× 103 0.4× 62 2.1k
Yu Takahashi Japan 25 1.2k 1.2× 135 0.4× 57 0.2× 200 0.6× 188 0.7× 62 1.9k

Countries citing papers authored by Sven Eyckerman

Since Specialization
Citations

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

Fields of papers citing papers by Sven Eyckerman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sven Eyckerman

This figure shows the co-authorship network connecting the top 25 collaborators of Sven Eyckerman. A scholar is included among the top collaborators of Sven Eyckerman 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 Sven Eyckerman. Sven Eyckerman 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.
Pauwels, Jarne, et al.. (2025). A 96‐Well Ultrafiltration Approach for the High‐Throughput Proteome Analysis of Extracellular Vesicles Isolated From Conditioned Medium. Journal of Extracellular Vesicles. 14(7). e70103–e70103.
2.
Verhee, Annick, René Houtman, Diana Melchers, et al.. (2024). Selective Modulation of the Human Glucocorticoid Receptor Compromises GR Chromatin Occupancy and Recruitment of p300/CBP and the Mediator Complex. Molecular & Cellular Proteomics. 23(3). 100741–100741. 6 indexed citations
3.
Fijałkowska, Daria, An Staes, Marnik Vuylsteke, et al.. (2023). N-terminal proteoforms may engage in different protein complexes. Life Science Alliance. 6(8). e202301972–e202301972. 4 indexed citations
4.
Dieu, Marc, et al.. (2023). Endogenous TOM20 Proximity Labeling: A Swiss-Knife for the Study of Mitochondrial Proteins in Human Cells. International Journal of Molecular Sciences. 24(11). 9604–9604. 3 indexed citations
5.
Paepe, Boél De, Jasper Anckaert, Nurten Yigit, et al.. (2023). mTOR Inhibition Enhances Delivery and Activity of Antisense Oligonucleotides in Uveal Melanoma Cells. Nucleic Acid Therapeutics. 33(4). 248–264. 2 indexed citations
6.
Peelman, Frank, et al.. (2023). Deep mutational scanning of proteins in mammalian cells. Cell Reports Methods. 3(11). 100641–100641. 12 indexed citations
7.
8.
Leirs, Karen, et al.. (2022). Engineered tracrRNA for enabling versatile CRISPR-dCas9-based biosensing concepts. Biosensors and Bioelectronics. 206. 114140–114140. 15 indexed citations
9.
Pauwels, Jarne, Daria Fijałkowska, Sven Eyckerman, & Kris Gevaert. (2021). Mass spectrometry and the cellular surfaceome. Mass Spectrometry Reviews. 41(5). 804–841. 30 indexed citations
10.
Najm, Paul, Mikhail Steklov, Raj Nayan Sewduth, et al.. (2021). Loss-of-Function Mutations in TRAF7 and KLF4 Cooperatively Activate RAS-Like GTPase Signaling and Promote Meningioma Development. Cancer Research. 81(16). 4218–4229. 16 indexed citations
11.
Sutter, Delphine De, et al.. (2017). Robust Generation of Knock-in Cell Lines Using CRISPR-Cas9 and rAAV-assisted Repair Template Delivery. BIO-PROTOCOL. 7(7). e2211–e2211. 2 indexed citations
12.
Eyckerman, Sven, Annick Verhee, Leentje De Ceuninck, et al.. (2016). Trapping mammalian protein complexes in viral particles. Nature Communications. 7(1). 11416–11416. 34 indexed citations
13.
Naessens, Evelien, Anouk Van Nuffel, Karin Weening, et al.. (2016). HIV-1 Vpr N-terminal tagging affects alternative splicing of the viral genome. Scientific Reports. 6(1). 34573–34573. 9 indexed citations
14.
Staes, An, Delphine De Sutter, Elien Vandermarliere, et al.. (2016). An extra dimension in protein tagging by quantifying universal proteotypic peptides using targeted proteomics. Scientific Reports. 6(1). 27220–27220. 12 indexed citations
15.
Acker, Tim Van, Sven Eyckerman, Lieselotte Vande Walle, et al.. (2013). The Small GTPase Arf6 Is Essential for the Tram/Trif Pathway in TLR4 Signaling. Journal of Biological Chemistry. 289(3). 1364–1376. 26 indexed citations
16.
Deka, Jürgen, Norbert Wiedemann, Pascale Anderle, et al.. (2010). Bcl9/Bcl9l Are Critical for Wnt-Mediated Regulation of Stem Cell Traits in Colon Epithelium and Adenocarcinomas. Cancer Research. 70(16). 6619–6628. 102 indexed citations
17.
Lievens, Sam, Sven Eyckerman, Irma Lemmens, & Jan Tavernier. (2010). Large-scale protein interactome mapping: strategies and opportunities. Expert Review of Proteomics. 7(5). 679–690. 28 indexed citations
18.
Brack, Andrew S., Jürgen Deka, Sven Eyckerman, et al.. (2009). BCL9 is an essential component of canonical Wnt signaling that mediates the differentiation of myogenic progenitors during muscle regeneration. Developmental Biology. 335(1). 93–105. 75 indexed citations
19.
Erkeland, Stefan J., Lambertus H.J. Aarts, Onno Roovers, et al.. (2006). Novel role of WD40 and SOCS box protein-2 in steady-state distribution of granulocyte colony-stimulating factor receptor and G-CSF-controlled proliferation and differentiation signaling. Oncogene. 26(14). 1985–1994. 22 indexed citations
20.
Lievens, Sam, et al.. (2006). Two-hybrid and its recent adaptations. Drug Discovery Today Technologies. 3(3). 317–324. 5 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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