George Ueda

1.9k total citations · 1 hit paper
9 papers, 440 citations indexed

About

George Ueda is a scholar working on Molecular Biology, Structural Biology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, George Ueda has authored 9 papers receiving a total of 440 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 2 papers in Structural Biology and 2 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in George Ueda's work include Protein Structure and Dynamics (3 papers), RNA and protein synthesis mechanisms (3 papers) and Monoclonal and Polyclonal Antibodies Research (2 papers). George Ueda is often cited by papers focused on Protein Structure and Dynamics (3 papers), RNA and protein synthesis mechanisms (3 papers) and Monoclonal and Polyclonal Antibodies Research (2 papers). George Ueda collaborates with scholars based in United States, Switzerland and Hong Kong. George Ueda's co-authors include David Baker, Jorge A. Fallas, William Sheffler, Scott E. Boyken, Michael D. Vahey, Frank DiMaio, James U. Bowie, Peilong Lu, Duyoung Min and Zibo Chen and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Chemistry.

In The Last Decade

George Ueda

9 papers receiving 432 citations

Hit Papers

De novo design of modular protein hydrogels with programm... 2024 2026 2025 2024 10 20 30

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
George Ueda United States 9 336 77 55 50 46 9 440
Yoshimasa Kawaguchi Japan 12 560 1.7× 64 0.8× 49 0.9× 28 0.6× 91 2.0× 46 866
Glenna Foight United States 7 311 0.9× 66 0.9× 42 0.8× 16 0.3× 20 0.4× 10 388
Žiga Strmšek Slovenia 11 466 1.4× 39 0.5× 56 1.0× 81 1.6× 112 2.4× 17 545
Fabio Lapenta Slovenia 11 475 1.4× 45 0.6× 61 1.1× 88 1.8× 126 2.7× 18 591
Dawn R. Christianson United States 10 308 0.9× 40 0.5× 77 1.4× 75 1.5× 76 1.7× 13 542
Marco Mravic United States 11 347 1.0× 58 0.8× 23 0.4× 20 0.4× 57 1.2× 21 583
Edward A. Esposito United States 9 356 1.1× 103 1.3× 29 0.5× 24 0.5× 65 1.4× 14 510
Raghavendra Vasudeva Murthy India 12 215 0.6× 69 0.9× 26 0.5× 16 0.3× 69 1.5× 22 398
Tatiana A. Zdobnova Russia 11 203 0.6× 119 1.5× 97 1.8× 32 0.6× 73 1.6× 21 387
Misao Akishiba Japan 9 482 1.4× 21 0.3× 60 1.1× 23 0.5× 76 1.7× 14 542

Countries citing papers authored by George Ueda

Since Specialization
Citations

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

Fields of papers citing papers by George Ueda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of George Ueda

This figure shows the co-authorship network connecting the top 25 collaborators of George Ueda. A scholar is included among the top collaborators of George Ueda 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 George Ueda. George Ueda 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.
Jiang, Hanlun, Kevin M. Jude, Jorge A. Fallas, et al.. (2024). De novo design of buttressed loops for sculpting protein functions. Nature Chemical Biology. 20(8). 974–980. 14 indexed citations
2.
Mout, Rubul, Ross C. Bretherton, Justin Decarreau, et al.. (2024). De novo design of modular protein hydrogels with programmable intra- and extracellular viscoelasticity. Proceedings of the National Academy of Sciences. 121(6). e2309457121–e2309457121. 32 indexed citations breakdown →
3.
Ellis, Daniel, Annie Dosey, Seyhan Boyoglu-Barnum, et al.. (2023). Antigen spacing on protein nanoparticles influences antibody responses to vaccination. Cell Reports. 42(12). 113552–113552. 14 indexed citations
4.
Courbet, Alexis, Jesse M. Hansen, Yang Hsia, et al.. (2022). Computational design of mechanically coupled axle-rotor protein assemblies. Science. 376(6591). 383–390. 30 indexed citations
5.
Zhao, Yan Ting, Jorge A. Fallas, Shally Saini, et al.. (2021). F‐domain valency determines outcome of signaling through the angiopoietin pathway. EMBO Reports. 22(12). e53471–e53471. 12 indexed citations
6.
Vulovic, Ivan, Qing Yao, Young‐Jun Park, et al.. (2021). Generation of ordered protein assemblies using rigid three-body fusion. Proceedings of the National Academy of Sciences. 118(23). 22 indexed citations
7.
Mohan, Kritika, George Ueda, Kevin M. Jude, et al.. (2019). Topological control of cytokine receptor signaling induces differential effects in hematopoiesis. Science. 364(6442). 86 indexed citations
8.
Lu, Peilong, Duyoung Min, Frank DiMaio, et al.. (2018). Accurate computational design of multipass transmembrane proteins. Science. 359(6379). 1042–1046. 137 indexed citations
9.
Fallas, Jorge A., George Ueda, William Sheffler, et al.. (2016). Computational design of self-assembling cyclic protein homo-oligomers. Nature Chemistry. 9(4). 353–360. 93 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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