Shugo Nakamura
- Molecular Biology top 10%
- Materials Chemistry
- Atomic and Molecular Physics, and Optics
- Cell Biology top 10%
- Immunology
- Co-authors
- Kentaro ShimizuMitsunori IkeguchiTohru TeradaYuko HashimotoTakayasu KurataShinya TanakaKenkoh MuroyaSatoshi Hattori
- Topics
- Protein Structure and Dynamics (19 papers)RNA and protein synthesis mechanisms (14 papers)Machine Learning in Bioinformatics (12 papers)
- Journals
- ScienceProceedings of the National Academy of SciencesJournal of the American Chemical Society
- Partner nations
- JapanUnited StatesIran
In The Last Decade
Shugo Nakamura
68 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 129
- Molecular Biology 975
- Materials Chemistry 193
- Atomic and Molecular Physics, and Optics 150
- Cell Biology 148
- Immunology 130
Countries citing papers authored by Shugo Nakamura
This map shows the geographic impact of Shugo Nakamura'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 Shugo Nakamura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shugo Nakamura more than expected).
Fields of papers citing papers by Shugo Nakamura
This network shows the impact of papers produced by Shugo Nakamura. 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 Shugo Nakamura. The network helps show where Shugo Nakamura may publish in the future.
Co-authorship network of co-authors of Shugo Nakamura
This figure shows the co-authorship network connecting the top 25 collaborators of Shugo Nakamura. A scholar is included among the top collaborators of Shugo Nakamura based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shugo Nakamura. Shugo Nakamura is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 9 | |
| 3 | 0 | |
| 4 | 6 | |
| 5 | 11 | |
| 6 | 2 | |
| 7 | 37 | |
| 8 | 35 | |
| 9 | 18 | |
| 10 | 55 | |
| 11 | 38 | |
| 12 | 32 | |
| 13 | 11 | |
| 14 | 72 | |
| 15 | 9 | |
| 16 | 2 | |
| 17 | 6 | |
| 18 | Automatic Improvement of Scheduling Policies in Parsley Parallel Programming Environment. | 1 |
| 19 | 16 | |
| 20 | 21 |
About Shugo Nakamura
Shugo Nakamura is a scholar working on Molecular Biology, Sensory Systems and Computational Theory and Mathematics, having authored 73 papers that have together received 1.6k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (19 papers), RNA and protein synthesis mechanisms (14 papers) and Machine Learning in Bioinformatics (12 papers). The work is most often cited by research in Immunology and Allergy (110 citations), Molecular Biology (975 citations) and Pollution (121 citations). Shugo Nakamura has collaborated with scholars based in Japan, United States and Iran. Frequent co-authors include Kentaro Shimizu, Mitsunori Ikeguchi, Tohru Terada, Yuko Hashimoto, Takayasu Kurata, Shinya Tanaka, Kenkoh Muroya, Satoshi Hattori, Michiyuki Matsuda and Seishi Shimizu. Their work appears in journals such as Science, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.
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.