Takashi Natori
- Molecular Biology top 10%
- Physiology top 5%
- Surgery top 5%
- Endocrinology, Diabetes and Metabolism top 2%
- Epidemiology top 10%
- Co-authors
- Tsukasa HirashimaK KawanoYoichi SaitohSeijiro MoriS MoriMasao KurosumiKazuya KawanoShigehito Mori
- Topics
- T-cell and B-cell Immunology (16 papers)Monoclonal and Polyclonal Antibodies Research (11 papers)Immune Cell Function and Interaction (9 papers)
- Partner nations
- JapanUnited StatesUnited Kingdom
In The Last Decade
Takashi Natori
66 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 101
- Molecular Biology 875
- Physiology 737
- Surgery 719
- Endocrinology, Diabetes and Metabolism 450
- Epidemiology 409
Countries citing papers authored by Takashi Natori
This map shows the geographic impact of Takashi Natori'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 Takashi Natori with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takashi Natori more than expected).
Fields of papers citing papers by Takashi Natori
This network shows the impact of papers produced by Takashi Natori. 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 Takashi Natori. The network helps show where Takashi Natori may publish in the future.
Co-authorship network of co-authors of Takashi Natori
This figure shows the co-authorship network connecting the top 25 collaborators of Takashi Natori. A scholar is included among the top collaborators of Takashi Natori 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 Takashi Natori. Takashi Natori is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 5 | |
| 3 | 15 | |
| 4 | 46 | |
| 5 | 30 | |
| 6 | 203 | |
| 7 | 23 | |
| 8 | 19 | |
| 9 | 13 | |
| 10 | 7 | |
| 11 | 19 | |
| 12 | 2 | |
| 13 | 11 | |
| 14 | COMMON EPITOPE(S) AMONG RT1-D, I-E, AND DR ANTIGENS DEFINED BY A RAT MONOCLONAL ALLOANTIBODY HOK7 | 1 |
| 15 | 1 | |
| 16 | Immunochemical evidence of a tumor-specific surface antigen obtained by detergent solubilization of the membranes of a chemically induced sarcoma, meth-A. | 25 |
| 17 | 8 | |
| 18 | Component fragments obtained by acid dissociation of the alpha- glycoprotein associated with beta2-microglobulin in mouse plasma. | 10 |
| 19 | Basic structure of mouse histocompatibility antigens. | 5 |
| 20 | 41 |
About Takashi Natori
Takashi Natori is a scholar working on Transplantation, Immunology and Hematology, having authored 70 papers that have together received 2.7k indexed citations. Recurring topics across this work include T-cell and B-cell Immunology (16 papers), Monoclonal and Polyclonal Antibodies Research (11 papers) and Immune Cell Function and Interaction (9 papers). The work is most often cited by research in Endocrine and Autonomic Systems (272 citations), Physiology (737 citations) and Endocrinology, Diabetes and Metabolism (450 citations). Takashi Natori has collaborated with scholars based in Japan, United States and United Kingdom. Frequent co-authors include Tsukasa Hirashima, K Kawano, Yoichi Saitoh, Seijiro Mori, S Mori, Masao Kurosumi, Kazuya Kawano, Shigehito Mori, Miki Aizawa and David Pressman. Their work appears in journals such as The Journal of Immunology, JNCI Journal of the National Cancer Institute and Cancer.
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.