T Friedmann

1.8k total citations · 1 hit paper
9 papers, 1.4k citations indexed

About

T Friedmann is a scholar working on Molecular Biology, Genetics and Oncology. According to data from OpenAlex, T Friedmann has authored 9 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 6 papers in Genetics and 4 papers in Oncology. Recurrent topics in T Friedmann's work include Virus-based gene therapy research (6 papers), RNA Interference and Gene Delivery (3 papers) and CRISPR and Genetic Engineering (3 papers). T Friedmann is often cited by papers focused on Virus-based gene therapy research (6 papers), RNA Interference and Gene Delivery (3 papers) and CRISPR and Genetic Engineering (3 papers). T Friedmann collaborates with scholars based in United States. T Friedmann's co-authors include Jane C. Burns, J K Yee, Wolfgang Driever, Michelle Burrascano, Klaus Roemer, Shantanu Sharma, T J Kipps, Atsushi Miyanohara, Thomas J. Kipps and Marie M. Cantwell and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Blood and Journal of Lipid Research.

In The Last Decade

T Friedmann

9 papers receiving 1.4k citations

Hit Papers

Vesicular stomatitis virus G glycoprotein pseudotyped ret... 1993 2026 2004 2015 1993 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
T Friedmann United States 8 947 945 212 192 189 9 1.4k
Bheem M. Bhat Canada 18 1.2k 1.2× 721 0.8× 330 1.6× 215 1.1× 291 1.5× 35 1.7k
Michelle Burrascano United States 5 811 0.9× 820 0.9× 145 0.7× 182 0.9× 179 0.9× 7 1.2k
M P Calos United States 18 1.7k 1.8× 777 0.8× 366 1.7× 205 1.1× 374 2.0× 24 2.4k
Munchen Eiden United States 16 634 0.7× 812 0.9× 190 0.9× 126 0.7× 206 1.1× 22 1.4k
J M Heard France 14 569 0.6× 634 0.7× 95 0.4× 122 0.6× 172 0.9× 20 1.2k
Geoff Symonds Australia 25 1.5k 1.6× 648 0.7× 389 1.8× 211 1.1× 144 0.8× 98 2.2k
Denise R. Shaw United States 21 858 0.9× 392 0.4× 298 1.4× 99 0.5× 108 0.6× 34 1.4k
Christoph Volpers Germany 15 542 0.6× 587 0.6× 154 0.7× 103 0.5× 289 1.5× 19 962
Carole Evelegh Canada 21 777 0.8× 793 0.8× 418 2.0× 204 1.1× 186 1.0× 28 1.4k
Junona Moroianu United States 27 1.9k 2.0× 470 0.5× 202 1.0× 107 0.6× 400 2.1× 44 2.4k

Countries citing papers authored by T Friedmann

Since Specialization
Citations

This map shows the geographic impact of T Friedmann'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 T Friedmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T Friedmann more than expected).

Fields of papers citing papers by T Friedmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of T Friedmann

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

All Works

9 of 9 papers shown
1.
Sharma, Shantanu, Marie M. Cantwell, Thomas J. Kipps, & T Friedmann. (1996). Efficient infection of a human T-cell line and of human primary peripheral blood leukocytes with a pseudotyped retrovirus vector.. Proceedings of the National Academy of Sciences. 93(21). 11842–11847. 39 indexed citations
2.
Sharma, Shantanu, et al.. (1996). Adenovirus vector infection of chronic lymphocytic leukemia B cells. Blood. 88(12). 4676–4683. 78 indexed citations
3.
Burns, Jane C., T Friedmann, Wolfgang Driever, Michelle Burrascano, & J K Yee. (1993). Vesicular stomatitis virus G glycoprotein pseudotyped retroviral vectors: concentration to very high titer and efficient gene transfer into mammalian and nonmammalian cells.. Proceedings of the National Academy of Sciences. 90(17). 8033–8037. 1117 indexed citations breakdown →
4.
Roemer, Klaus & T Friedmann. (1992). Concepts and strategies for human gene therapy. European Journal of Biochemistry. 208(2). 211–225. 61 indexed citations
5.
Levine, Fred & T Friedmann. (1991). Gene therapy techniques. Current Opinion in Biotechnology. 2(6). 840–844. 9 indexed citations
6.
Friedmann, T. (1991). Genetically modified tumor-infiltrating lymphocytes for cancer therapy.. PubMed. 3(7). 271–4. 6 indexed citations
7.
Miyanohara, Atsushi, et al.. (1990). Post-transcriptional regulation of retroviral vector-transduced low density lipoprotein receptor activity.. Journal of Lipid Research. 31(12). 2167–2178. 27 indexed citations
8.
Miyanohara, Atsushi, et al.. (1988). Efficient expression of retroviral vector-transduced human low density lipoprotein (LDL) receptor in LDL receptor-deficient rabbit fibroblasts in vitro.. Proceedings of the National Academy of Sciences. 85(17). 6538–6542. 46 indexed citations
9.
Friedmann, T. (1971). In Vitro Reassembly of Shell-Like Particles from Disrupted Polyoma Virus. Proceedings of the National Academy of Sciences. 68(10). 2574–2578. 26 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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