Tomoki Naito
Impact in
- Media Technology top 2%
- Vehicle License Plate Recognition
- Cell Biology top 5%
- Cellular transport and secretion
Papers in
-
- Lipid Membrane Structure and Behavior 11
- Receptor Mechanisms and Signaling 3
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- Cellular transport and secretion 9
- Endoplasmic Reticulum Stress and Disease 3
- Co-authors
- Yasunori Saheki (9 shared papers)Hye‐Won Shin (7 shared papers)Kazuhisa Nakayama (6 shared papers)Hiroyuki Takatsu (6 shared papers)T. Tsukada (3 shared papers)K. Yamada (3 shared papers)Shin Yamamoto (3 shared papers)Kazuhiro Kozuka (3 shared papers)
- Journals
- Nature Communications (4 papers)Molecular Biology of the Cell (2 papers)Neuroscience (2 papers)Physics of Plasmas (2 papers)The EMBO Journal (2 papers)
- Partner nations
- JapanSingaporeUnited States
In The Last Decade
Tomoki Naito
31 papers receiving 933 citations
Peers
Comparison fields: 5 of 90
- Media Technology 169
- Cell Biology 219
- Computer Vision and Pattern Recognition 177
- Physiology 36
- Molecular Biology 448
Countries citing papers authored by Tomoki Naito
This map shows the geographic impact of Tomoki Naito'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 Tomoki Naito with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomoki Naito more than expected).
Fields of papers citing papers by Tomoki Naito
This network shows the impact of papers produced by Tomoki Naito. 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 Tomoki Naito. The network helps show where Tomoki Naito may publish in the future.
Co-authors
The 25 scholars most cited alongside Tomoki Naito, 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 33 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2000 | 159 | |
| 2 | 2019 | 113 | |
| 3 | 2015 | 68 | |
| 4 | 2013 | 64 | |
| 5 | 2018 | 58 | |
| 6 | 2021 | 52 | |
| 7 | 2018 | 51 | |
| 8 | 2017 | 47 | |
| 9 | 2000 | 44 | |
| 10 | 2007 | 40 | |
| 11 | 2021 | 37 | |
| 12 | 2021 | 31 | |
| 13 | 2023 | 29 | |
| 14 | 2016 | 29 | |
| 15 | 2023 | 22 | |
| 16 | 2002 | 21 | |
| 17 | 2020 | 20 | |
| 18 | 2021 | 17 | |
| 19 | 2019 | 10 | |
| 20 | 2021 | 8 |
About Tomoki Naito
Tomoki Naito is a scholar working on Molecular Biology, Cell Biology, Computer Vision and Pattern Recognition, Media Technology and Aerospace Engineering, having authored 33 papers that have together received 959 indexed citations. Recurring topics across this work include Lipid Membrane Structure and Behavior (11 papers), Cellular transport and secretion (9 papers), Vehicle License Plate Recognition (4 papers), Plasma Diagnostics and Applications (4 papers), Antenna Design and Analysis (4 papers), Endoplasmic Reticulum Stress and Disease (3 papers), Image and Object Detection Techniques (3 papers) and Receptor Mechanisms and Signaling (3 papers). The work is most often cited by research in Media Technology (169 citations), Cell Biology (219 citations), Computer Vision and Pattern Recognition (177 citations), Physiology (36 citations) and Molecular Biology (448 citations). Tomoki Naito has collaborated with scholars based in Japan, Singapore and United States. Frequent co-authors include Yasunori Saheki, Hye‐Won Shin, Kazuhisa Nakayama, Hiroyuki Takatsu, T. Tsukada, K. Yamada, Shin Yamamoto, Kazuhiro Kozuka, Dylan Hong Zheng Koh and Bilge Ercan. Their work appears in journals such as Nature Communications, Molecular Biology of the Cell, Neuroscience, Physics of Plasmas and The EMBO Journal.
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.