Dai Maruyama
Impact in
-
- Lymphoma Diagnosis and Treatment
- Genetics top 5%
- Chronic Lymphocytic Leukemia Research
Papers in
-
- Lymphoma Diagnosis and Treatment 99
- Oncology 82
- Viral-associated cancers and disorders 39
- CAR-T cell therapy research 23
- Co-authors
- Kensei Tobinai (94 shared papers)Yukio Kobayashi (55 shared papers)Akiko Miyagi Maeshima (59 shared papers)Wataru Munakata (59 shared papers)Shinichi Makita (45 shared papers)Suguru Fukuhara (51 shared papers)Hirokazu Taniguchi (35 shared papers)Tatsuya Suzuki (37 shared papers)
- Journals
- Blood (20 papers)International Journal of Hematology (20 papers)Cancer Science (17 papers)Japanese Journal of Clinical Oncology (16 papers)Journal of Clinical Oncology (8 papers)
- Partner nations
- JapanUnited StatesUnited Kingdom
In The Last Decade
Dai Maruyama
141 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 74
- Pathology and Forensic Medicine 831
- Genetics 252
- Oncology 605
- Neurology 205
- Hematology 158
Countries citing papers authored by Dai Maruyama
This map shows the geographic impact of Dai Maruyama'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 Dai Maruyama with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dai Maruyama more than expected).
Fields of papers citing papers by Dai Maruyama
This network shows the impact of papers produced by Dai Maruyama. 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 Dai Maruyama. The network helps show where Dai Maruyama may publish in the future.
Co-authors
The 25 scholars most cited alongside Dai Maruyama, 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 160 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 58 | |
| 2 | 2016 | 57 | |
| 3 | 2007 | 54 | |
| 4 | 2014 | 49 | |
| 5 | 2017 | 47 | |
| 6 | 2016 | 44 | |
| 7 | 2008 | 35 | |
| 8 | 2018 | 34 | |
| 9 | 2008 | 33 | |
| 10 | 2012 | 31 | |
| 11 | 2008 | 31 | |
| 12 | 2021 | 27 | |
| 13 | 2010 | 27 | |
| 14 | 2010 | 25 | |
| 15 | 2017 | 25 | |
| 16 | 2017 | 25 | |
| 17 | 2018 | 25 | |
| 18 | 2010 | 25 | |
| 19 | 2015 | 24 | |
| 20 | 2010 | 24 |
About Dai Maruyama
Dai Maruyama is a scholar working on Pathology and Forensic Medicine, Oncology, Genetics, Hematology and Immunology, having authored 160 papers that have together received 1.5k indexed citations. Recurring topics across this work include Lymphoma Diagnosis and Treatment (99 papers), Viral-associated cancers and disorders (39 papers), Chronic Lymphocytic Leukemia Research (36 papers), CAR-T cell therapy research (23 papers), CNS Lymphoma Diagnosis and Treatment (18 papers), Multiple Myeloma Research and Treatments (18 papers), T-cell and Retrovirus Studies (12 papers) and Cutaneous lymphoproliferative disorders research (10 papers). The work is most often cited by research in Pathology and Forensic Medicine (831 citations), Genetics (252 citations), Oncology (605 citations), Neurology (205 citations) and Hematology (158 citations). Dai Maruyama has collaborated with scholars based in Japan, United States and United Kingdom. Frequent co-authors include Kensei Tobinai, Yukio Kobayashi, Akiko Miyagi Maeshima, Wataru Munakata, Shinichi Makita, Suguru Fukuhara, Hirokazu Taniguchi, Tatsuya Suzuki, Takashi Watanabe and Michinori Ogura. Their work appears in journals such as Blood, International Journal of Hematology, Cancer Science, Japanese Journal of Clinical Oncology and Journal of Clinical Oncology.
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.