Kennosuke Wada
Impact in
- Molecular Medicine top 5%
- Curcumin's Biomedical Applications
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- Genomics and Phylogenetic Studies
- RNA and protein synthesis mechanisms
- Genomics and Chromatin Dynamics
- Machine Learning in Bioinformatics
Papers in
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- Genomics and Phylogenetic Studies 16
- RNA and protein synthesis mechanisms 8
- Genomics and Chromatin Dynamics 6
- Machine Learning in Bioinformatics 5
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- Chromosomal and Genetic Variations 6
- Co-authors
- Toshimichi Ikemura (23 shared papers)Yoshiko Wada (17 shared papers)Takashi Abe (13 shared papers)Shin‐ichi Aota (1 shared paper)Yuki Iwasaki (13 shared papers)Yasuhiro Wada (3 shared papers)Ikuo Tooyama (2 shared papers)Daijiro Yanagisawa (1 shared paper)
- Journals
- Genes & Genetic Systems (4 papers)DNA Research (2 papers)Scientific Reports (1 paper)Gene (1 paper)Life Science Alliance (1 paper)
- Partner nations
- JapanTaiwanUnited States
In The Last Decade
Kennosuke Wada
23 papers receiving 417 citations
Peers
Comparison fields: 5 of 76
- Molecular Medicine 75
- Molecular Biology 266
- Physiology 77
- Plant Science 90
- Genetics 61
Countries citing papers authored by Kennosuke Wada
This map shows the geographic impact of Kennosuke Wada'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 Kennosuke Wada with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kennosuke Wada more than expected).
Fields of papers citing papers by Kennosuke Wada
This network shows the impact of papers produced by Kennosuke Wada. 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 Kennosuke Wada. The network helps show where Kennosuke Wada may publish in the future.
Co-authors
The 25 scholars most cited alongside Kennosuke Wada, 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 26 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 131 | |
| 2 | 1991 | 65 | |
| 3 | 1990 | 55 | |
| 4 | 1993 | 32 | |
| 5 | 2013 | 25 | |
| 6 | 2013 | 16 | |
| 7 | 2011 | 13 | |
| 8 | 2016 | 12 | |
| 9 | 2020 | 10 | |
| 10 | 2021 | 9 | |
| 11 | 2013 | 9 | |
| 12 | 2020 | 7 | |
| 13 | 2015 | 7 | |
| 14 | 2014 | 6 | |
| 15 | 2009 | 6 | |
| 16 | 2021 | 5 | |
| 17 | 2022 | 4 | |
| 18 | 2017 | 4 | |
| 19 | 2022 | 3 | |
| 20 | 2023 | 2 |
About Kennosuke Wada
Kennosuke Wada is a scholar working on Molecular Biology, Plant Science, Infectious Diseases, Epidemiology and Genetics, having authored 26 papers that have together received 425 indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (16 papers), RNA and protein synthesis mechanisms (8 papers), Genomics and Chromatin Dynamics (6 papers), Chromosomal and Genetic Variations (6 papers), Machine Learning in Bioinformatics (5 papers), Influenza Virus Research Studies (4 papers), Viral gastroenteritis research and epidemiology (4 papers) and Bacteriophages and microbial interactions (3 papers). The work is most often cited by research in Molecular Medicine (75 citations), Molecular Biology (266 citations), Physiology (77 citations), Plant Science (90 citations) and Genetics (61 citations). Kennosuke Wada has collaborated with scholars based in Japan, Taiwan and United States. Frequent co-authors include Toshimichi Ikemura, Yoshiko Wada, Takashi Abe, Shin‐ichi Aota, Yuki Iwasaki, Yasuhiro Wada, Ikuo Tooyama, Daijiro Yanagisawa, Hiroyasu Taguchi and Fuminori Kato. Their work appears in journals such as Genes & Genetic Systems, DNA Research, Scientific Reports, Gene and Life Science Alliance.
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.