Toshiyuki Namba
- Pharmacology top 0.5%
- Inflammatory mediators and NSAID effects 8
- Biochemistry top 1%
- Genetics top 2%
- Evolution and Genetic Dynamics 14
- Physiology top 5%
- Nitric Oxide and Endothelin Effects 3
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- Mathematical and Theoretical Epidemiology and Ecology Models 13
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- Receptor Mechanisms and Signaling 10
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- Evolutionary Game Theory and Cooperation 10
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- Plant and animal studies 9
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- Animal Ecology and Behavior Studies 6
- Co-authors
- Shuh NarumiyaYukihiko SugimotoMasahiko NegishiAtsushi IchikawaAkiko HondaAkinobu IrieAkiko WatabeYasunori Hayashi
- Cited by
- PharmacologyBiochemistryGenetics
- Journals
- Journal of Biological Chemistry (7 papers)Circulation (1 paper)The Journal of Experimental Medicine (1 paper)
- Partner nations
- JapanUnited StatesSwitzerland
In The Last Decade
Toshiyuki Namba
41 papers receiving 2.7k citations
Peers
Comparison fields: 5 of 121
- Pharmacology 1.2k
- Biochemistry 395
- Genetics 771
- Cellular and Molecular Neuroscience 402
- Physiology 505
Countries citing papers authored by Toshiyuki Namba
This map shows the geographic impact of Toshiyuki Namba'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 Toshiyuki Namba with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Toshiyuki Namba more than expected).
Fields of papers citing papers by Toshiyuki Namba
This network shows the impact of papers produced by Toshiyuki Namba. 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 Toshiyuki Namba. The network helps show where Toshiyuki Namba may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Toshiyuki Namba, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 1 | |
| 2 | 2020 | 8 | |
| 3 | 2020 | 5 | |
| 4 | 2018 | 9 | |
| 5 | 2014 | 4 | |
| 6 | 2014 | 7 | |
| 7 | 2008 | 75 | |
| 8 | 2004 | 19 | |
| 9 | 2001 | 1 | |
| 10 | 1999 | 38 | |
| 11 | 1994 | 61 | |
| 12 | 1994 | 152 | |
| 13 | 1994 | 196 | |
| 14 | 1993 | 192 | |
| 15 | 1992 | 320 | |
| 16 | 1991 | 22 | |
| 17 | 1989 | 17 | |
| 18 | 1986 | 11 | |
| 19 | 1980 | 56 | |
| 20 | 1980 | 7 |
About Toshiyuki Namba
Toshiyuki Namba is a scholar working on Modeling and Simulation, Genetics and Pharmacology, having authored 42 papers that have together received 2.8k indexed citations. Recurring topics across this work include Evolution and Genetic Dynamics (14 papers), Mathematical and Theoretical Epidemiology and Ecology Models (13 papers), Receptor Mechanisms and Signaling (10 papers), Evolutionary Game Theory and Cooperation (10 papers), Plant and animal studies (9 papers), Inflammatory mediators and NSAID effects (8 papers), Animal Ecology and Behavior Studies (6 papers) and Nitric Oxide and Endothelin Effects (3 papers). The work is most often cited by research in Pharmacology (1.2k citations), Biochemistry (395 citations) and Genetics (771 citations). Toshiyuki Namba has collaborated with scholars based in Japan, United States and Switzerland. Frequent co-authors include Shuh Narumiya, Yukihiko Sugimoto, Masahiko Negishi, Atsushi Ichikawa, Akiko Honda, Akinobu Irie, Akiko Watabe, Yasunori Hayashi, Akira Kakizuka and Seigo Ito. Their work appears in journals such as Journal of Biological Chemistry, Circulation and The Journal of Experimental Medicine.
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.