Takuya Kishimoto
Impact in
- Cell Biology top 2%
- Cellular transport and secretion
- Physiology top 2%
- Calcium signaling and nucleotide metabolism
Papers in
-
- Lipid Membrane Structure and Behavior 10
- Cell Biology 13
- Cellular transport and secretion 13
- Co-authors
- Haruo Kasai (15 shared papers)Noriko Takahashi (11 shared papers)Tomomi Nemoto (8 shared papers)Takashi Kadowaki (2 shared papers)Hiroyasu Hatakeyama (7 shared papers)Yasushi Miyashita (4 shared papers)Shinichi Aizawa (1 shared paper)Yoshio Yazaki (1 shared paper)
- Journals
- The Journal of Physiology (6 papers)Science (2 papers)The EMBO Journal (2 papers)Veterinary Pathology (2 papers)Applied Sciences (1 paper)
- Partner nations
- JapanTaiwanUnited States
In The Last Decade
Takuya Kishimoto
43 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 112
- Cell Biology 543
- Physiology 135
- Surgery 592
- Biophysics 78
- Molecular Biology 785
Countries citing papers authored by Takuya Kishimoto
This map shows the geographic impact of Takuya Kishimoto'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 Takuya Kishimoto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takuya Kishimoto more than expected).
Fields of papers citing papers by Takuya Kishimoto
This network shows the impact of papers produced by Takuya Kishimoto. 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 Takuya Kishimoto. The network helps show where Takuya Kishimoto may publish in the future.
Co-authors
The 25 scholars most cited alongside Takuya Kishimoto, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 1999 | 400 | |
| 2 | 2002 | 219 | |
| 3 | 2004 | 95 | |
| 4 | 1996 | 72 | |
| 5 | 1997 | 72 | |
| 6 | 2005 | 60 | |
| 7 | 2007 | 53 | |
| 8 | 1999 | 51 | |
| 9 | 2006 | 40 | |
| 10 | 2001 | 39 | |
| 11 | 2005 | 36 | |
| 12 | 2002 | 36 | |
| 13 | 2005 | 34 | |
| 14 | 2006 | 33 | |
| 15 | 2005 | 24 | |
| 16 | 2019 | 22 | |
| 17 | 2016 | 21 | |
| 18 | 2018 | 16 | |
| 19 | 2021 | 15 | |
| 20 | 2021 | 11 |
About Takuya Kishimoto
Takuya Kishimoto is a scholar working on Molecular Biology, Cell Biology, Surgery, Pulmonary and Respiratory Medicine and Cellular and Molecular Neuroscience, having authored 46 papers that have together received 1.5k indexed citations. Recurring topics across this work include Cellular transport and secretion (13 papers), Lipid Membrane Structure and Behavior (10 papers), Pancreatic function and diabetes (7 papers), Microstructure and Mechanical Properties of Steels (4 papers), Advanced Fluorescence Microscopy Techniques (3 papers), Veterinary Oncology Research (3 papers), Erythrocyte Function and Pathophysiology (3 papers) and Shape Memory Alloy Transformations (3 papers). The work is most often cited by research in Cell Biology (543 citations), Physiology (135 citations), Surgery (592 citations), Biophysics (78 citations) and Molecular Biology (785 citations). Takuya Kishimoto has collaborated with scholars based in Japan, Taiwan and United States. Frequent co-authors include Haruo Kasai, Noriko Takahashi, Tomomi Nemoto, Takashi Kadowaki, Hiroyasu Hatakeyama, Yasushi Miyashita, Shinichi Aizawa, Yoshio Yazaki, Yoshiharu Tsubamoto and Shigeo Murayama. Their work appears in journals such as The Journal of Physiology, Science, The EMBO Journal, Veterinary Pathology and Applied Sciences.
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.