Naokuni Uike
Impact in
- Immunology top 0.5%
- T-cell and Retrovirus Studies
- Immune Cell Function and Interaction
- Pathology and Forensic Medicine top 0.5%
- Lymphoma Diagnosis and Treatment
Papers in
- Hematology 42
- Acute Myeloid Leukemia Research 16
- Genetics 33
- Chronic Lymphocytic Leukemia Research 28
- Co-authors
- Kensei TobinaiAtae UtsunomiyaMichinori OguraMasao TomonagaTakashi IshidaToshihiro MiyamotoKunihiro TsukasakiRyuzo Ueda
- Journals
- Blood (23 papers)International Journal of Hematology (14 papers)British Journal of Haematology (8 papers)Journal of Clinical Oncology (7 papers)Cancer Science (7 papers)
- Partner nations
- JapanUnited StatesGreece
In The Last Decade
Naokuni Uike
149 papers receiving 5.0k citations
Hit Papers
Peers
Comparison fields: 5 of 96
- Immunology 2.5k
- Pathology and Forensic Medicine 1.8k
- Genetics 852
- Agronomy and Crop Science 807
- Hematology 772
Countries citing papers authored by Naokuni Uike
This map shows the geographic impact of Naokuni Uike'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 Naokuni Uike with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Naokuni Uike more than expected).
Fields of papers citing papers by Naokuni Uike
This network shows the impact of papers produced by Naokuni Uike. 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 Naokuni Uike. The network helps show where Naokuni Uike may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Naokuni Uike, 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 | 2020 | 48 | |
| 2 | 2019 | 6 | |
| 3 | 2018 | 15 | |
| 4 | 2016 | 7 | |
| 5 | 2014 | 34 | |
| 6 | 2014 | 4 | |
| 7 | 2012 | 9 | |
| 8 | 2011 | 45 | |
| 9 | 2010 | 50 | |
| 10 | 2009 | 25 | |
| 11 | 2009 | 41 | |
| 12 | 2008 | 1 | |
| 13 | 2008 | 7 | |
| 14 | 2008 | 43 | |
| 15 | 2004 | 34 | |
| 16 | 2002 | 44 | |
| 17 | 2002 | 39 | |
| 18 | 2002 | 102 | |
| 19 | 2001 | 35 | |
| 20 | [A comparative study of imipenem/cilastatin sodium BID vs QID in the treatment of infections associated with hematopoietic disorders]. | 1994 | 1 |
About Naokuni Uike
Naokuni Uike is a scholar working on Hematology, Genetics, Immunology, Pathology and Forensic Medicine and Agronomy and Crop Science, having authored 151 papers that have together received 5.1k indexed citations. Recurring topics across this work include T-cell and Retrovirus Studies (50 papers), Lymphoma Diagnosis and Treatment (46 papers), Vector-Borne Animal Diseases (33 papers), Chronic Lymphocytic Leukemia Research (28 papers), Animal Disease Management and Epidemiology (23 papers), Viral-associated cancers and disorders (21 papers), Acute Myeloid Leukemia Research (16 papers) and Cutaneous lymphoproliferative disorders research (13 papers). The work is most often cited by research in Immunology (2.5k citations), Pathology and Forensic Medicine (1.8k citations), Genetics (852 citations), Agronomy and Crop Science (807 citations) and Hematology (772 citations). Naokuni Uike has collaborated with scholars based in Japan, United States and Greece. Frequent co-authors include Kensei Tobinai, Atae Utsunomiya, Michinori Ogura, Masao Tomonaga, Takashi Ishida, Toshihiro Miyamoto, Kunihiro Tsukasaki, Ryuzo Ueda, Koichi Akashi and Koichi Ohshima. Their work appears in journals such as Blood, International Journal of Hematology, British Journal of Haematology, Journal of Clinical Oncology and Cancer Science.
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.