Sam Strickson

1.2k total citations
10 papers, 894 citations indexed

About

Sam Strickson is a scholar working on Immunology, Cancer Research and Molecular Biology. According to data from OpenAlex, Sam Strickson has authored 10 papers receiving a total of 894 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Immunology, 8 papers in Cancer Research and 6 papers in Molecular Biology. Recurrent topics in Sam Strickson's work include NF-κB Signaling Pathways (8 papers), interferon and immune responses (5 papers) and Ubiquitin and proteasome pathways (3 papers). Sam Strickson is often cited by papers focused on NF-κB Signaling Pathways (8 papers), interferon and immune responses (5 papers) and Ubiquitin and proteasome pathways (3 papers). Sam Strickson collaborates with scholars based in United Kingdom, Nepal and South Sudan. Sam Strickson's co-authors include Philip Cohen, Christoph H. Emmerich, J. Simon C. Arthur, David Komander, Alban Ordureau, Patrick G. A. Pedrioli, Axel Knebel, Maria Stella Ritorto, David G. Campbell and Natalia Shpiro and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Scientific Reports.

In The Last Decade

Sam Strickson

10 papers receiving 886 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sam Strickson United Kingdom 7 564 439 252 180 121 10 894
Lucia Taraborrelli United Kingdom 8 769 1.4× 535 1.2× 321 1.3× 225 1.3× 106 0.9× 8 958
Sebastian Kupka United Kingdom 7 646 1.1× 429 1.0× 258 1.0× 159 0.9× 113 0.9× 7 790
Marja Kreike Belgium 14 515 0.9× 517 1.2× 448 1.8× 184 1.0× 82 0.7× 19 939
Katharina Wolter Germany 5 678 1.2× 787 1.8× 145 0.6× 205 1.1× 173 1.4× 14 1.3k
Aleksandra Bankovacki Australia 10 566 1.0× 434 1.0× 155 0.6× 117 0.7× 160 1.3× 10 798
Helena Draberova Czechia 9 580 1.0× 429 1.0× 247 1.0× 138 0.8× 84 0.7× 9 753
Daniel R. Beisner United States 10 594 1.1× 666 1.5× 126 0.5× 191 1.1× 99 0.8× 11 1.0k
Shaogang Sun United States 15 662 1.2× 332 0.8× 375 1.5× 195 1.1× 218 1.8× 17 1.2k
Rosa Barreira da Silva United States 12 521 0.9× 722 1.6× 139 0.6× 456 2.5× 246 2.0× 16 1.3k
Romania Stilo Italy 17 366 0.6× 412 0.9× 322 1.3× 112 0.6× 95 0.8× 37 818

Countries citing papers authored by Sam Strickson

Since Specialization
Citations

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

Fields of papers citing papers by Sam Strickson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sam Strickson

This figure shows the co-authorship network connecting the top 25 collaborators of Sam Strickson. A scholar is included among the top collaborators of Sam Strickson 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 Sam Strickson. Sam Strickson 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
2.
Bennett, Kyle J.M., Sambit K. Nanda, Sam Strickson, et al.. (2022). Why are the phenotypes of TRAF6 knock-in and TRAF6 knock-out mice so different?. PLoS ONE. 17(2). e0263151–e0263151. 2 indexed citations
3.
Queiroz, Rayner M. L., Johanna S. Rees, Sam Strickson, et al.. (2022). Proteomic analysis in primary T cells reveals IL-7 alters T cell receptor thresholding via CYTIP/cytohesin/LFA-1 localisation and activation.. Apollo (University of Cambridge). 1 indexed citations
4.
Strickson, Sam, Christoph H. Emmerich, Eddy T. H. Goh, et al.. (2017). Roles of the TRAF6 and Pellino E3 ligases in MyD88 and RANKL signaling. Proceedings of the National Academy of Sciences. 114(17). E3481–E3489. 88 indexed citations
5.
Cohen, Philip & Sam Strickson. (2017). The role of hybrid ubiquitin chains in the MyD88 and other innate immune signalling pathways. Cell Death and Differentiation. 24(7). 1153–1159. 87 indexed citations
6.
Vollmer, Stefan, Sam Strickson, Tinghu Zhang, et al.. (2017). The mechanism of activation of IRAK1 and IRAK4 by interleukin-1 and Toll-like receptor agonists. Biochemical Journal. 474(12). 2027–2038. 65 indexed citations
7.
Bakshi, Siddharth, et al.. (2017). Identification of TBK1 complexes required for the phosphorylation of IRF3 and the production of interferon β. Biochemical Journal. 474(7). 1163–1174. 43 indexed citations
8.
McGuire, Victoria A., Christoph H. Emmerich, Sam Strickson, et al.. (2016). Dimethyl fumarate blocks pro-inflammatory cytokine production via inhibition of TLR induced M1 and K63 ubiquitin chain formation. Scientific Reports. 6(1). 31159–31159. 87 indexed citations
9.
Emmerich, Christoph H., Alban Ordureau, Sam Strickson, et al.. (2013). Activation of the canonical IKK complex by K63/M1-linked hybrid ubiquitin chains. Proceedings of the National Academy of Sciences. 110(38). 15247–15252. 346 indexed citations
10.
Strickson, Sam, David G. Campbell, Christoph H. Emmerich, et al.. (2013). The anti-inflammatory drug BAY 11-7082 suppresses the MyD88-dependent signalling network by targeting the ubiquitin system. Biochemical Journal. 451(3). 427–437. 170 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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