Clare E. Thomas

4.0k total citations · 1 hit paper
18 papers, 3.2k citations indexed

About

Clare E. Thomas is a scholar working on Genetics, Molecular Biology and Oncology. According to data from OpenAlex, Clare E. Thomas has authored 18 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Genetics, 11 papers in Molecular Biology and 5 papers in Oncology. Recurrent topics in Clare E. Thomas's work include Virus-based gene therapy research (13 papers), RNA Interference and Gene Delivery (8 papers) and CAR-T cell therapy research (5 papers). Clare E. Thomas is often cited by papers focused on Virus-based gene therapy research (13 papers), RNA Interference and Gene Delivery (8 papers) and CAR-T cell therapy research (5 papers). Clare E. Thomas collaborates with scholars based in United Kingdom, United States and France. Clare E. Thomas's co-authors include Mark A. Kay, Anja Ehrhardt, María G. Castro, Theresa A. Storm, Pedro R. Löwenstein, Zan Huang, Gudrun Schiedner, Stefan Kochanek, Hiroyuki Nakai and Sally Fuess and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Experimental Medicine and The Journal of Cell Biology.

In The Last Decade

Clare E. Thomas

18 papers receiving 3.2k citations

Hit Papers

Progress and problems with the use of viral vectors for g... 2003 2026 2010 2018 2003 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Clare E. Thomas United Kingdom 13 2.5k 1.8k 394 295 274 18 3.2k
Nikunj V. Somia United States 16 3.4k 1.3× 1.5k 0.9× 301 0.8× 219 0.7× 245 0.9× 32 4.2k
Larry A. Couture United States 30 2.1k 0.9× 1.3k 0.7× 509 1.3× 370 1.3× 422 1.5× 47 3.5k
Phillip W.L. Tai United States 25 3.2k 1.3× 2.1k 1.2× 542 1.4× 255 0.9× 218 0.8× 53 4.1k
Christian Leborgne France 28 2.6k 1.1× 1.9k 1.1× 524 1.3× 163 0.6× 165 0.6× 49 3.5k
Alessandra Recchia Italy 28 2.1k 0.8× 1.4k 0.8× 844 2.1× 510 1.7× 165 0.6× 45 3.4k
Charles Coutelle United Kingdom 37 3.1k 1.2× 2.2k 1.2× 201 0.5× 239 0.8× 151 0.6× 154 4.3k
R. Scott McIvor United States 39 3.2k 1.3× 2.1k 1.2× 812 2.1× 355 1.2× 150 0.5× 162 4.7k
Anja Ehrhardt Germany 32 4.2k 1.7× 3.1k 1.7× 863 2.2× 345 1.2× 332 1.2× 114 5.4k
Johann Meyer Germany 23 2.9k 1.2× 1.3k 0.8× 870 2.2× 570 1.9× 312 1.1× 46 4.0k
Marinee Chuah Belgium 43 3.9k 1.6× 2.7k 1.5× 1.2k 3.0× 505 1.7× 284 1.0× 114 5.6k

Countries citing papers authored by Clare E. Thomas

Since Specialization
Citations

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

Fields of papers citing papers by Clare E. Thomas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Clare E. Thomas. 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 Clare E. Thomas. The network helps show where Clare E. Thomas may publish in the future.

Co-authorship network of co-authors of Clare E. Thomas

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

All Works

18 of 18 papers shown
1.
Thomas, Clare E.. (2019). Teacher and student experiences in learning. 2(1). 3–3. 2 indexed citations
2.
Chen, Yung‐Yi, Hannah C. Jeffery, Stuart Hunter, et al.. (2016). Human intrahepatic regulatory T cells are functional, require IL‐2 from effector cells for survival, and are susceptible to Fas ligand‐mediated apoptosis. Hepatology. 64(1). 138–150. 71 indexed citations
3.
Puntel, Mariana, Kurt M. Kroeger, Nicholas Sanderson, et al.. (2010). Gene Transfer into Rat Brain Using Adenoviral Vectors. Current Protocols in Neuroscience. 50(1). Unit 4.24–Unit 4.24. 33 indexed citations
4.
Barcia, Carlos, Clare E. Thomas, James F. Curtin, et al.. (2006). In vivo mature immunological synapses forming SMACs mediate clearance of virally infected astrocytes from the brain. The Journal of Experimental Medicine. 203(9). 2095–2107. 81 indexed citations
6.
Barcia, Carlos, Clare E. Thomas, James F. Curtin, et al.. (2006). In vivo mature immunological synapses forming SMACs mediate clearance of virally infected astrocytes from the brain. The Journal of Cell Biology. 174(6). i10–i10. 4 indexed citations
7.
Thomas, Clare E., Theresa A. Storm, Zan Huang, & Mark A. Kay. (2004). Rapid Uncoating of Vector Genomes Is the Key toEfficient Liver Transduction with Pseudotyped Adeno-Associated VirusVectors. Journal of Virology. 78(6). 3110–3122. 291 indexed citations
8.
Thomas, Clare E., Anja Ehrhardt, & Mark A. Kay. (2003). Progress and problems with the use of viral vectors for gene therapy. Nature Reviews Genetics. 4(5). 346–358. 1971 indexed citations breakdown →
9.
Hurtado‐Lorenzo, Andres, Anne David, Clare E. Thomas, María G. Castro, & Pedro R. Löwenstein. (2003). Use of Recombinant Adenovirus for Gene Transfer into the Rat Brain: Evaluation of Gene Transfer Efficiency, Toxicity, and Inflammatory and Immune Reactions. Humana Press eBooks. 76. 113–134. 8 indexed citations
10.
Grimm, Dirk, Shangzhen Zhou, Hiroyuki Nakai, et al.. (2003). Preclinical in vivo evaluation of pseudotyped adeno-associated virus vectors for liver gene therapy. Blood. 102(7). 2412–2419. 178 indexed citations
11.
Lowenstein, P R, Clare E. Thomas, Pablo Umaña, et al.. (2002). [17] High-capacity, helper-dependent, “gutless” adenoviral vectors for gene transfer into brain. Methods in enzymology on CD-ROM/Methods in enzymology. 346. 292–311. 22 indexed citations
12.
Nakai, Hiroyuki, Clare E. Thomas, Theresa A. Storm, et al.. (2002). A Limited Number of Transducible Hepatocytes Restricts a Wide-Range Linear Vector Dose Response in Recombinant Adeno-Associated Virus-Mediated Liver Transduction. Journal of Virology. 76(22). 11343–11349. 83 indexed citations
15.
Thomas, Clare E., Gudrun Schiedner, Stefan Kochanek, María G. Castro, & P R Lowenstein. (2001). Preexisting Antiadenoviral Immunity Is Not a Barrier to Efficient and Stable Transduction of the Brain, Mediated by Novel High-Capacity Adenovirus Vectors. Human Gene Therapy. 12(7). 839–846. 80 indexed citations
16.
Thomas, Clare E., Gudrun Schiedner, Stefan Kochanek, María G. Castro, & Pedro R. Löwenstein. (2000). Peripheral infection with adenovirus causes unexpected long-term brain inflammation in animals injected intracranially with first-generation, but not with high-capacity, adenovirus vectors: Toward realistic long-term neurological gene therapy for chronic diseases. Proceedings of the National Academy of Sciences. 97(13). 7482–7487. 205 indexed citations
17.
Thomas, Clare E., et al.. (2000). Gene Transfer into Rat Brain Using Adenoviral Vectors. Current Protocols in Neuroscience. 13(1). Unit 4.24–Unit 4.24. 8 indexed citations
18.
Löwenstein, Pedro R., Clare E. Thomas, & María G. Castro. (1999). Politically correct gene therapy? A “clean environment” improves gene delivery to the brain!. Gene Therapy. 6(4). 463–464. 4 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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