Eiichi Nakayama
- Immunology top 0.5%
- Molecular Biology top 5%
- Oncology top 1%
- Epidemiology top 2%
- Radiology, Nuclear Medicine and Imaging top 2%
- Co-authors
- Akiko UenakaIsao TawaraHiroshi ShikuShimon SakaguchiTakashi FujitaShin OnizukaJun ShimizuHeiichiro Udono
- Topics
- Immunotherapy and Immune Responses (92 papers)Immune Cell Function and Interaction (45 papers)T-cell and B-cell Immunology (41 papers)
- Cited by
- ImmunologyOncologyMicrobiology
- Partner nations
- JapanUnited StatesAustralia
In The Last Decade
Eiichi Nakayama
181 papers receiving 7.0k citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Immunology 4.2k
- Molecular Biology 2.2k
- Oncology 1.9k
- Epidemiology 993
- Radiology, Nuclear Medicine and Imaging 803
Countries citing papers authored by Eiichi Nakayama
This map shows the geographic impact of Eiichi Nakayama'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 Eiichi Nakayama with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eiichi Nakayama more than expected).
Fields of papers citing papers by Eiichi Nakayama
This network shows the impact of papers produced by Eiichi Nakayama. 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 Eiichi Nakayama. The network helps show where Eiichi Nakayama may publish in the future.
Co-authorship network of co-authors of Eiichi Nakayama
This figure shows the co-authorship network connecting the top 25 collaborators of Eiichi Nakayama. A scholar is included among the top collaborators of Eiichi Nakayama 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 Eiichi Nakayama. Eiichi Nakayama is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 29 | |
| 2 | 179 | |
| 3 | Immune-mediated antitumor effect by type 2 diabetes drug, metforminbreakdown → | 433 |
| 4 | 20 | |
| 5 | 48 | |
| 6 | 43 | |
| 7 | 87 | |
| 8 | 109 | |
| 9 | Antibiotic susceptibility and T type identification of Streptococcus pyogenes isolated from pediatric outpatients with pharyngotonsillitis | 2 |
| 10 | 4 | |
| 11 | 2 | |
| 12 | 78 | |
| 13 | 6 | |
| 14 | 21 | |
| 15 | 22 | |
| 16 | 30 | |
| 17 | Rapid Loss of Graft Immunogenicity and Transient Hyporesponsiveness to The Donor Antigen After Rat Liver Transplantation | 3 |
| 18 | 4 | |
| 19 | 84 | |
| 20 | 20 |
About Eiichi Nakayama
Eiichi Nakayama is a scholar working on Immunology, Immunology and Allergy and Radiology, Nuclear Medicine and Imaging, having authored 184 papers that have together received 7.2k indexed citations. Recurring topics across this work include Immunotherapy and Immune Responses (92 papers), Immune Cell Function and Interaction (45 papers) and T-cell and B-cell Immunology (41 papers). The work is most often cited by research in Immunology (4.2k citations), Oncology (1.9k citations) and Microbiology (367 citations). Eiichi Nakayama has collaborated with scholars based in Japan, United States and Australia. Frequent co-authors include Akiko Uenaka, Isao Tawara, Hiroshi Shiku, Shimon Sakaguchi, Takashi Fujita, Shin Onizuka, Jun Shimizu, Heiichiro Udono, Toshiro Ono and Lloyd J. Old. Their work appears in journals such as Proceedings of the National Academy of Sciences, The Lancet and Nature Communications.
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.