Takayuki Teramoto
- Aging top 1%
- Genetics, Aging, and Longevity in Model Organisms 12
- Biophysics top 1%
- Cell Image Analysis Techniques 4
- Advanced Fluorescence Microscopy Techniques 3
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- Photoreceptor and optogenetics research 2
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- Circadian rhythm and melatonin 6
- Sensory Systems top 5%
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- Single-cell and spatial transcriptomics 4
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- Distributed and Parallel Computing Systems 4
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- Experimental Learning in Engineering 2
- Co-authors
- Takeshi IshiharaManabi FujiwaraTakeharu NagaiMasahiro NakanoYongxin ZhaoYu-Fen ChangAhmed S. AbdelfattahRobert E. Campbell
- Partner nations
- JapanUnited StatesIreland
In The Last Decade
Takayuki Teramoto
28 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 118
- Aging 258
- Biophysics 290
- Cellular and Molecular Neuroscience 515
- Endocrine and Autonomic Systems 160
- Sensory Systems 95
Countries citing papers authored by Takayuki Teramoto
This map shows the geographic impact of Takayuki Teramoto'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 Takayuki Teramoto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takayuki Teramoto more than expected).
Fields of papers citing papers by Takayuki Teramoto
This network shows the impact of papers produced by Takayuki Teramoto. 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 Takayuki Teramoto. The network helps show where Takayuki Teramoto may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Takayuki Teramoto, 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 | 2025 | 1 | |
| 2 | 2024 | 1 | |
| 3 | 2023 | 1 | |
| 4 | 2022 | 10 | |
| 5 | 2021 | 59 | |
| 6 | 2020 | 20 | |
| 7 | 2018 | 13 | |
| 8 | 2018 | 21 | |
| 9 | 2017 | 12 | |
| 10 | 2016 | 19 | |
| 11 | 2016 | 27 | |
| 12 | An Expanded Palette of Genetically Encoded Ca 2+ Indicatorsbreakdown → | 2011 | 993 |
| 13 | 2010 | 24 | |
| 14 | 2010 | 20 | |
| 15 | 2007 | 55 | |
| 16 | 2006 | 52 | |
| 17 | 2005 | 53 | |
| 18 | 2004 | 9 | |
| 19 | 2000 | 37 | |
| 20 | 1998 | 16 |
About Takayuki Teramoto
Takayuki Teramoto is a scholar working on Aging, Endocrine and Autonomic Systems and Biophysics, having authored 30 papers that have together received 1.5k indexed citations. Recurring topics across this work include Genetics, Aging, and Longevity in Model Organisms (12 papers), Circadian rhythm and melatonin (6 papers), Single-cell and spatial transcriptomics (4 papers), Distributed and Parallel Computing Systems (4 papers), Cell Image Analysis Techniques (4 papers), Advanced Fluorescence Microscopy Techniques (3 papers), Photoreceptor and optogenetics research (2 papers) and Experimental Learning in Engineering (2 papers). The work is most often cited by research in Aging (258 citations), Biophysics (290 citations) and Cellular and Molecular Neuroscience (515 citations). Takayuki Teramoto has collaborated with scholars based in Japan, United States and Ireland. Frequent co-authors include Takeshi Ishihara, Manabi Fujiwara, Takeharu Nagai, Masahiro Nakano, Yongxin Zhao, Yu-Fen Chang, Ahmed S. Abdelfattah, Robert E. Campbell, Jiahui Wu and Kouichi Iwasaki. Their work appears in journals such as Science, Bioinformatics and PLoS ONE.
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.