Da‐Kang Shen

609 total citations
11 papers, 239 citations indexed

About

Da‐Kang Shen is a scholar working on Endocrinology, Genetics and Infectious Diseases. According to data from OpenAlex, Da‐Kang Shen has authored 11 papers receiving a total of 239 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Endocrinology, 7 papers in Genetics and 4 papers in Infectious Diseases. Recurrent topics in Da‐Kang Shen's work include Escherichia coli research studies (7 papers), Bacterial Genetics and Biotechnology (6 papers) and Bacteriophages and microbial interactions (3 papers). Da‐Kang Shen is often cited by papers focused on Escherichia coli research studies (7 papers), Bacterial Genetics and Biotechnology (6 papers) and Bacteriophages and microbial interactions (3 papers). Da‐Kang Shen collaborates with scholars based in United Kingdom, Japan and China. Da‐Kang Shen's co-authors include Ariel Blocker, Benoı̂t Polack, Isabel Martínez‐Argudo, Madiha Derouazi, Bertrand Toussaint, Isabel Murillo, Hiroaki Nishioka, Carolin Wagner, Saroj Saurya and Keiichi Namba and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and PLoS ONE.

In The Last Decade

Da‐Kang Shen

11 papers receiving 239 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Da‐Kang Shen United Kingdom 10 151 113 77 55 53 11 239
Olivia Arizmendi United States 6 173 1.1× 122 1.1× 73 0.9× 56 1.0× 85 1.6× 7 287
Maj Brodmann Switzerland 7 169 1.1× 83 0.7× 145 1.9× 77 1.4× 38 0.7× 7 314
Marie‐Ève Charbonneau Canada 11 161 1.1× 151 1.3× 143 1.9× 37 0.7× 42 0.8× 11 330
Nathan P. Bullen Canada 6 226 1.5× 103 0.9× 152 2.0× 112 2.0× 50 0.9× 10 385
Yassine Cherrak Switzerland 7 259 1.7× 114 1.0× 143 1.9× 98 1.8× 18 0.3× 11 367
Biswanath Jana India 13 196 1.3× 114 1.0× 183 2.4× 76 1.4× 52 1.0× 25 382
Anna M. Kolodziejek United States 8 101 0.7× 196 1.7× 142 1.8× 21 0.4× 25 0.5× 12 322
Milena Jaskólska Switzerland 6 159 1.1× 161 1.4× 165 2.1× 97 1.8× 33 0.6× 7 363
Brian C. Russo United States 11 96 0.6× 106 0.9× 123 1.6× 18 0.3× 49 0.9× 17 255

Countries citing papers authored by Da‐Kang Shen

Since Specialization
Citations

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

Fields of papers citing papers by Da‐Kang Shen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Da‐Kang Shen. 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 Da‐Kang Shen. The network helps show where Da‐Kang Shen may publish in the future.

Co-authorship network of co-authors of Da‐Kang Shen

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

All Works

11 of 11 papers shown
1.
Powers, Kyle T., Sathish K.N. Yadav, Beate Amthor, et al.. (2021). Blasticidin S inhibits mammalian translation and enhances production of protein encoded by nonsense mRNA. Nucleic Acids Research. 49(13). 7665–7679. 11 indexed citations
2.
Bordignon, Enrica, Da‐Kang Shen, Xia Liu, et al.. (2017). Steps for Shigella Gatekeeper Protein MxiC Function in Hierarchical Type III Secretion Regulation. Journal of Biological Chemistry. 292(5). 1705–1723. 19 indexed citations
3.
Makino, Fumiaki, Da‐Kang Shen, Naoko Kajimura, et al.. (2016). The Architecture of the Cytoplasmic Region of Type III Secretion Systems. Scientific Reports. 6(1). 33341–33341. 30 indexed citations
4.
Shen, Da‐Kang & Ariel Blocker. (2016). MxiA, MxiC and IpaD Regulate Substrate Selection and Secretion Mode in the T3SS of Shigella flexneri. PLoS ONE. 11(5). e0155141–e0155141. 20 indexed citations
5.
Verasdonck, Joeri, Da‐Kang Shen, Christopher J. Arthur, et al.. (2015). Reassessment of MxiH subunit orientation and fold within native Shigella T3SS needles using surface labelling and solid-state NMR. Journal of Structural Biology. 192(3). 441–448. 11 indexed citations
6.
Shen, Da‐Kang, Fumiaki Makino, Takayuki Kato, et al.. (2014). Three‐dimensional electron microscopy reconstruction and cysteine‐mediated crosslinking provide a model of the type III secretion system needle tip complex. Molecular Microbiology. 95(1). 31–50. 40 indexed citations
7.
8.
Shen, Da‐Kang, Saroj Saurya, Carolin Wagner, Hiroaki Nishioka, & Ariel Blocker. (2010). Domains of the Shigella flexneri Type III Secretion System IpaB Protein Involved in Secretion Regulation. Infection and Immunity. 78(12). 4999–5010. 36 indexed citations
9.
Shen, Da‐Kang, Lauriane E. Quenee, Lauriane Kühn, et al.. (2008). Orf1/SpcS Chaperones ExoS for Type Three Secretion by Pseudomonas aeruginosa. Biomedical and Environmental Sciences. 21(2). 103–109. 20 indexed citations
10.
Shen, Da‐Kang, et al.. (2008). High-cell-density regulation of the Pseudomonas aeruginosa type III secretion system: implications for tryptophan catabolites. Microbiology. 154(8). 2195–2208. 35 indexed citations
11.
Wang, Zhaojun, Wei Hu, Da‐Kang Shen, et al.. (2003). [Prokaryotic expression of gene encoding Schistosoma japonicum SjE16 and its potential application in immunodiagnosis].. PubMed. 21(2). 76–9. 2 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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