Takanori Ueda
- Molecular Biology top 5%
- Hematology top 1%
- Oncology top 5%
- Surgery top 10%
- Immunology top 10%
- Co-authors
- Akira YoshidaJong‐Dae LeeHiromichi IwasakiTakahiro YamauchiHiroyasu UzuiHiromasa ShimizuYoshimasa UrasakiSatoshi Ikegaya
- Topics
- Acute Myeloid Leukemia Research (50 papers)Acute Lymphoblastic Leukemia research (32 papers)Chronic Lymphocytic Leukemia Research (28 papers)
- Cited by
- HematologyNephrologyGenetics
- Partner nations
- JapanChinaUnited States
In The Last Decade
Takanori Ueda
294 papers receiving 3.9k citations
Peers
Comparison fields: 5 of 148
- Molecular Biology 1.5k
- Hematology 675
- Oncology 642
- Surgery 412
- Immunology 403
Countries citing papers authored by Takanori Ueda
This map shows the geographic impact of Takanori Ueda'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 Takanori Ueda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takanori Ueda more than expected).
Fields of papers citing papers by Takanori Ueda
This network shows the impact of papers produced by Takanori Ueda. 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 Takanori Ueda. The network helps show where Takanori Ueda may publish in the future.
Co-authorship network of co-authors of Takanori Ueda
This figure shows the co-authorship network connecting the top 25 collaborators of Takanori Ueda. A scholar is included among the top collaborators of Takanori Ueda 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 Takanori Ueda. Takanori Ueda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 10 | |
| 4 | 7 | |
| 5 | 5 | |
| 6 | Low Latency Data Stream Processing on Multi-Core CPU Environments | 0 |
| 7 | Early relapse is associated with high serum soluble interleukin-2 receptor after the sixth cycle of R-CHOP chemotherapy in patients with advanced diffuse large B-cell lymphoma. | 13 |
| 8 | 1 | |
| 9 | 0 | |
| 10 | Overcoming imatinib resistance using Src inhibitor CGP76030, Abl inhibitor nilotinib, and Abl/Lyn inhibitor INNO-406 in newly established K562 variants with bcr-abl gene amplification. | 3 |
| 11 | Copyright violation detection system for Web texts | 0 |
| 12 | New quantitation method for monitoring cytarabine incorporated into DNA of leukemic cells from patients receiving cytarabine therapy | 0 |
| 13 | 73 | |
| 14 | 6 | |
| 15 | 65 | |
| 16 | 10 | |
| 17 | 1 | |
| 18 | 14 | |
| 19 | 2 | |
| 20 | 2 |
About Takanori Ueda
Takanori Ueda is a scholar working on Hematology, Genetics and Nephrology, having authored 310 papers that have together received 4.1k indexed citations. Recurring topics across this work include Acute Myeloid Leukemia Research (50 papers), Acute Lymphoblastic Leukemia research (32 papers) and Chronic Lymphocytic Leukemia Research (28 papers). The work is most often cited by research in Hematology (675 citations), Nephrology (238 citations) and Genetics (345 citations). Takanori Ueda has collaborated with scholars based in Japan, China and United States. Frequent co-authors include Akira Yoshida, Jong‐Dae Lee, Hiromichi Iwasaki, Takahiro Yamauchi, Hiroyasu Uzui, Hiromasa Shimizu, Yoshimasa Urasaki, Satoshi Ikegaya, Yasuhiko Mitsuke and Tõru Nakamura. Their work appears in journals such as The Lancet, Journal of Biological Chemistry and Circulation.
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.