Carsten A. Raabe

1.1k total citations
25 papers, 803 citations indexed

About

Carsten A. Raabe is a scholar working on Molecular Biology, Immunology and Genetics. According to data from OpenAlex, Carsten A. Raabe has authored 25 papers receiving a total of 803 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 7 papers in Immunology and 6 papers in Genetics. Recurrent topics in Carsten A. Raabe's work include Genomics and Phylogenetic Studies (7 papers), S100 Proteins and Annexins (6 papers) and RNA and protein synthesis mechanisms (6 papers). Carsten A. Raabe is often cited by papers focused on Genomics and Phylogenetic Studies (7 papers), S100 Proteins and Annexins (6 papers) and RNA and protein synthesis mechanisms (6 papers). Carsten A. Raabe collaborates with scholars based in Germany, Malaysia and China. Carsten A. Raabe's co-authors include Timofey S. Rozhdestvensky, Juergen Brosius, Thean‐Hock Tang, Chee Hock Hoe, Ursula Rescher, Thean‐Hock Tang, Boris V. Skryabin, Jürgen Brosius, Xinping Li and Zoltán Konthur and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Scientific Reports.

In The Last Decade

Carsten A. Raabe

25 papers receiving 786 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Carsten A. Raabe Germany 16 650 223 164 107 100 25 803
Igor Ruiz de los Mozos United Kingdom 19 1.1k 1.8× 200 0.9× 200 1.2× 39 0.4× 126 1.3× 26 1.4k
Romualdas Vaisvila United States 15 1.0k 1.6× 120 0.5× 312 1.9× 22 0.2× 116 1.2× 20 1.2k
Robert Goldstone United Kingdom 19 496 0.8× 63 0.3× 121 0.7× 123 1.1× 59 0.6× 32 945
Arun Kommadath Canada 15 263 0.4× 128 0.6× 180 1.1× 69 0.6× 32 0.3× 29 557
Anne Keriel France 14 670 1.0× 79 0.4× 271 1.7× 67 0.6× 40 0.4× 28 991
Katja Koeppen United States 18 733 1.1× 41 0.2× 84 0.5× 83 0.8× 120 1.2× 34 1.1k
Xiaoye Wang China 14 453 0.7× 34 0.2× 82 0.5× 56 0.5× 144 1.4× 30 890
Shengwei Hu China 14 405 0.6× 201 0.9× 172 1.0× 29 0.3× 23 0.2× 48 574
Zsolt Balázs Hungary 16 404 0.6× 92 0.4× 147 0.9× 84 0.8× 100 1.0× 32 826
David Neves Brazil 15 304 0.5× 51 0.2× 95 0.6× 78 0.7× 43 0.4× 29 665

Countries citing papers authored by Carsten A. Raabe

Since Specialization
Citations

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

Fields of papers citing papers by Carsten A. Raabe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Carsten A. Raabe. 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 Carsten A. Raabe. The network helps show where Carsten A. Raabe may publish in the future.

Co-authorship network of co-authors of Carsten A. Raabe

This figure shows the co-authorship network connecting the top 25 collaborators of Carsten A. Raabe. A scholar is included among the top collaborators of Carsten A. Raabe 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 Carsten A. Raabe. Carsten A. Raabe 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.
Soehnlein, Oliver, et al.. (2024). Comparative analysis of formyl peptide receptor 1 and formyl peptide receptor 2 reveals shared and preserved signalling profiles. British Journal of Pharmacology. 182(20). 4911–4925. 2 indexed citations
2.
Grewal, Thomas, et al.. (2021). Annexin Animal Models—From Fundamental Principles to Translational Research. International Journal of Molecular Sciences. 22(7). 3439–3439. 40 indexed citations
3.
Raabe, Carsten A., et al.. (2021). A Comprehensive Review of Genetically Engineered Mouse Models for Prader-Willi Syndrome Research. International Journal of Molecular Sciences. 22(7). 3613–3613. 14 indexed citations
4.
Li, Xinping, et al.. (2020). Circular RNA Encoded Amyloid Beta peptides—A Novel Putative Player in Alzheimer’s Disease. Cells. 9(10). 2196–2196. 37 indexed citations
5.
König, Gabriele M., et al.. (2020). Exploring Biased Agonism at FPR1 as a Means to Encode Danger Sensing. Cells. 9(4). 1054–1054. 9 indexed citations
6.
Citartan, Marimuthu, et al.. (2019). Rapid detection of porcine DNA in processed food samples using a streamlined DNA extraction method combined with the SYBR Green real-time PCR assay. Food Chemistry. 309. 125654–125654. 17 indexed citations
7.
Li, Xinping, Carsten A. Raabe, Di Cui, et al.. (2019). A universal approach to investigate circRNA protein coding function. Scientific Reports. 9(1). 11684–11684. 28 indexed citations
8.
Raabe, Carsten A., et al.. (2018). Bacterial regulatory RNAs: complexity, function, and putative drug targeting. Critical Reviews in Biochemistry and Molecular Biology. 53(4). 335–355. 7 indexed citations
9.
Citartan, Marimuthu, et al.. (2017). RNomic identification and evaluation of npcTB_6715, a non‐protein‐coding RNA gene as a potential biomarker for the detection of Mycobacterium tuberculosis. Journal of Cellular and Molecular Medicine. 21(10). 2276–2283. 5 indexed citations
10.
Rozhdestvensky, Timofey S., et al.. (2016). Maternal transcription of non-protein coding RNAs from the PWS-critical region rescues growth retardation in mice. Scientific Reports. 6(1). 20398–20398. 17 indexed citations
11.
Schmitz, Jürgen, Angela Noll, Carsten A. Raabe, et al.. (2016). Genome sequence of the basal haplorrhine primate Tarsius syrichta reveals unusual insertions. Nature Communications. 7(1). 12997–12997. 27 indexed citations
12.
Brosius, Jürgen & Carsten A. Raabe. (2016). What is an RNA?A top layer for RNA classification. RNA Biology. 13(2). 140–144. 26 indexed citations
13.
Raabe, Carsten A., et al.. (2014). Differential regulation of non-protein coding RNAs from Prader-Willi Syndrome locus. Scientific Reports. 4(1). 6445–6445. 36 indexed citations
14.
Raabe, Carsten A., Thean‐Hock Tang, Juergen Brosius, & Timofey S. Rozhdestvensky. (2013). Biases in small RNA deep sequencing data. Nucleic Acids Research. 42(3). 1414–1426. 161 indexed citations
15.
Raabe, Carsten A., et al.. (2013). Alternative Processing as Evolutionary Mechanism for the Origin of Novel Nonprotein Coding RNAs. Genome Biology and Evolution. 5(11). 2061–2071. 11 indexed citations
16.
Hoe, Chee Hock, Carsten A. Raabe, Timofey S. Rozhdestvensky, & Thean‐Hock Tang. (2013). Bacterial sRNAs: Regulation in stress. International Journal of Medical Microbiology. 303(5). 217–229. 105 indexed citations
17.
Raabe, Carsten A., Chee Hock Hoe, Karsten Becker, et al.. (2012). Transcription Analysis and Small Non-Protein Coding RNAs Associated with Bacterial Ribosomal Protein Operons. Current Medicinal Chemistry. 19(30). 5187–5198. 10 indexed citations
18.
Raabe, Carsten A., et al.. (2011). The rocks and shallows of deep RNA sequencing: Examples in the Vibrio cholerae RNome. RNA. 17(7). 1357–1366. 29 indexed citations
19.
Chinni, Suresh V., Carsten A. Raabe, Chee Hock Hoe, et al.. (2010). Experimental identification and characterization of 97 novel npcRNA candidates in Salmonella enterica serovar Typhi. Nucleic Acids Research. 38(17). 5893–5908. 43 indexed citations
20.
Raabe, Carsten A., Cecília P. Sanchez, Boris V. Skryabin, et al.. (2009). A global view of the nonprotein-coding transcriptome in Plasmodium falciparum. Nucleic Acids Research. 38(2). 608–617. 64 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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