Shinsuke Uda
Impact in
- Biophysics top 5%
- Cell Image Analysis Techniques
Papers in
-
- Gene Regulatory Network Analysis 13
- Bioinformatics and Genomic Networks 8
- Metabolism, Diabetes, and Cancer 3
- Microbial Metabolic Engineering and Bioproduction 3
- Metabolomics and Mass Spectrometry Studies 3
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- Computational Drug Discovery Methods 4
- Co-authors
- Shinya Kuroda (24 shared papers)Hiroyuki Kubota (20 shared papers)Yu Toyoshima (5 shared papers)Takeshi Saito (4 shared papers)Yasunori Komori (4 shared papers)Takaho Tsuchiya (4 shared papers)Jaehoon Chung (3 shared papers)Kazuhiro Fujita (2 shared papers)
- Journals
- PLoS ONE (6 papers)Cell Reports (4 papers)npj Systems Biology and Applications (3 papers)Science Signaling (2 papers)iScience (1 paper)
- Partner nations
- JapanUnited StatesAustralia
In The Last Decade
Shinsuke Uda
27 papers receiving 692 citations
Peers
Comparison fields: 5 of 109
- Biophysics 68
- Aging 16
- Molecular Biology 499
- Computational Mathematics 3
- Cellular and Molecular Neuroscience 73
Countries citing papers authored by Shinsuke Uda
This map shows the geographic impact of Shinsuke Uda'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 Shinsuke Uda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shinsuke Uda more than expected).
Fields of papers citing papers by Shinsuke Uda
This network shows the impact of papers produced by Shinsuke Uda. 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 Shinsuke Uda. The network helps show where Shinsuke Uda may publish in the future.
Co-authors
The 25 scholars most cited alongside Shinsuke Uda, 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 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 105 | |
| 2 | 2012 | 90 | |
| 3 | 2014 | 66 | |
| 4 | 2010 | 65 | |
| 5 | 2010 | 43 | |
| 6 | 2020 | 29 | |
| 7 | 2018 | 28 | |
| 8 | 2003 | 27 | |
| 9 | 2019 | 24 | |
| 10 | 2015 | 23 | |
| 11 | 2012 | 22 | |
| 12 | 2010 | 21 | |
| 13 | 2013 | 19 | |
| 14 | 2016 | 18 | |
| 15 | 2018 | 17 | |
| 16 | 2012 | 16 | |
| 17 | 2020 | 16 | |
| 18 | 2015 | 16 | |
| 19 | 2018 | 15 | |
| 20 | 2021 | 8 |
About Shinsuke Uda
Shinsuke Uda is a scholar working on Molecular Biology, Computational Theory and Mathematics, Surgery, Cellular and Molecular Neuroscience and Endocrinology, Diabetes and Metabolism, having authored 27 papers that have together received 694 indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (13 papers), Bioinformatics and Genomic Networks (8 papers), Computational Drug Discovery Methods (4 papers), Diabetes Management and Research (3 papers), Pancreatic function and diabetes (3 papers), Metabolism, Diabetes, and Cancer (3 papers), Microbial Metabolic Engineering and Bioproduction (3 papers) and Metabolomics and Mass Spectrometry Studies (3 papers). The work is most often cited by research in Biophysics (68 citations), Aging (16 citations), Molecular Biology (499 citations), Computational Mathematics (3 citations) and Cellular and Molecular Neuroscience (73 citations). Shinsuke Uda has collaborated with scholars based in Japan, United States and Australia. Frequent co-authors include Shinya Kuroda, Hiroyuki Kubota, Yu Toyoshima, Takeshi Saito, Yasunori Komori, Takaho Tsuchiya, Jaehoon Chung, Kazuhiro Fujita, Kanako Watanabe and Wataru Ogawa. Their work appears in journals such as PLoS ONE, Cell Reports, npj Systems Biology and Applications, Science Signaling and iScience.
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.