Shunji Uchimura
- Molecular Biology
- Hepatology top 2%
- Cancer Research top 10%
- Epidemiology
- Oncology
- Co-authors
- Yoshihiko HamamotoTakanobu MiyamotoNorio IizukaHisafumi Yamada‐OkabeMasaaki OkaTakao TamesaKenji HamadaAkira Tangoku
- Topics
- Gene expression and cancer classification (7 papers)Neural Networks and Applications (5 papers)Face and Expression Recognition (5 papers)
- Partner nations
- JapanUnited StatesSwitzerland
In The Last Decade
Shunji Uchimura
30 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 122
- Molecular Biology 574
- Hepatology 332
- Cancer Research 269
- Epidemiology 236
- Oncology 191
Countries citing papers authored by Shunji Uchimura
This map shows the geographic impact of Shunji Uchimura'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 Shunji Uchimura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shunji Uchimura more than expected).
Fields of papers citing papers by Shunji Uchimura
This network shows the impact of papers produced by Shunji Uchimura. 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 Shunji Uchimura. The network helps show where Shunji Uchimura may publish in the future.
Co-authorship network of co-authors of Shunji Uchimura
This figure shows the co-authorship network connecting the top 25 collaborators of Shunji Uchimura. A scholar is included among the top collaborators of Shunji Uchimura 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 Shunji Uchimura. Shunji Uchimura is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 0 | |
| 3 | Resampling based on geographic patterns of hepatitis virus infection reveals a common gene signature for early intrahepatic recurrence of hepatocellular carcinoma. | 4 |
| 4 | 35 | |
| 5 | 16 | |
| 6 | 56 | |
| 7 | 38 | |
| 8 | 29 | |
| 9 | 18 | |
| 10 | 15 | |
| 11 | Patterns of expression of cytochrome P450 genes in progression of hepatitis C virus-associated hepatocellular carcinoma. | 51 |
| 12 | 16 | |
| 13 | 70 | |
| 14 | 32 | |
| 15 | 44 | |
| 16 | 14 | |
| 17 | 32 | |
| 18 | 21 | |
| 19 | Comparison of Classifiers in Small Training Sample Size Situations for Pattern Recognition | 0 |
| 20 | Gabor Features for Handprinted Chinese Character Recognition | 2 |
About Shunji Uchimura
Shunji Uchimura is a scholar working on Hepatology, Computer Vision and Pattern Recognition and Cancer Research, having authored 32 papers that have together received 1.3k indexed citations. Recurring topics across this work include Gene expression and cancer classification (7 papers), Neural Networks and Applications (5 papers) and Face and Expression Recognition (5 papers). The work is most often cited by research in Hepatology (332 citations), Cancer Research (269 citations) and Computer Vision and Pattern Recognition (187 citations). Shunji Uchimura has collaborated with scholars based in Japan, United States and Switzerland. Frequent co-authors include Yoshihiko Hamamoto, Takanobu Miyamoto, Norio Iizuka, Hisafumi Yamada‐Okabe, Masaaki Oka, Takao Tamesa, Kenji Hamada, Akira Tangoku, Naohide Mori and Hironobu Nakayama. Their work appears in journals such as The Lancet, IEEE Transactions on Pattern Analysis and Machine Intelligence and Oncogene.
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.