Gen Kudo
Impact in
- Pharmacology top 5%
- Pharmacogenetics and Drug Metabolism
- Biochemistry top 10%
- Eicosanoids and Hypertension Pharmacology
Papers in
-
- S100 Proteins and Annexins 2
-
- Neuroscience and Neuropharmacology Research 3
- Co-authors
- Frank J. Gonzalez (3 shared papers)Shioko Kimura (2 shared papers)Hiroshi Yokota (1 shared paper)Tian J. Yang (1 shared paper)Ying‐Hue Lee (1 shared paper)Masaaki Miyata (1 shared paper)Pedro M. Fernández‐Salguero (1 shared paper)Harry V. Gelboin (1 shared paper)
- Journals
- NeuroImage (3 papers)Annals of Nuclear Medicine (2 papers)American Journal Of Pathology (1 paper)IEEE Robotics and Automation Letters (1 paper)Biochemical Pharmacology (1 paper)
- Partner nations
- JapanUnited StatesCanada
In The Last Decade
Gen Kudo
23 papers receiving 589 citations
Peers
Comparison fields: 5 of 96
- Pharmacology 99
- Biochemistry 56
- Immunology and Allergy 35
- Cancer Research 68
- Biological Psychiatry 11
Countries citing papers authored by Gen Kudo
This map shows the geographic impact of Gen Kudo'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 Gen Kudo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gen Kudo more than expected).
Fields of papers citing papers by Gen Kudo
This network shows the impact of papers produced by Gen Kudo. 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 Gen Kudo. The network helps show where Gen Kudo may publish in the future.
Co-authors
The 25 scholars most cited alongside Gen Kudo, 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 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 1999 | 161 | |
| 2 | 2001 | 101 | |
| 3 | 2010 | 69 | |
| 4 | 2003 | 66 | |
| 5 | 2010 | 33 | |
| 6 | 2013 | 33 | |
| 7 | 2011 | 24 | |
| 8 | 2008 | 23 | |
| 9 | 2010 | 16 | |
| 10 | 1988 | 11 | |
| 11 | 1990 | 11 | |
| 12 | 2002 | 9 | |
| 13 | 2023 | 8 | |
| 14 | 1998 | 6 | |
| 15 | 1995 | 6 | |
| 16 | 1993 | 5 | |
| 17 | 2010 | 5 | |
| 18 | 1988 | 4 | |
| 19 | 2008 | 3 | |
| 20 | 1994 | 2 |
About Gen Kudo
Gen Kudo is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience, Rheumatology, Hematology and Surgery, having authored 25 papers that have together received 602 indexed citations. Recurring topics across this work include Folate and B Vitamins Research (4 papers), Neuroscience and Neuropharmacology Research (3 papers), Blood Coagulation and Thrombosis Mechanisms (3 papers), Traumatic Brain Injury and Neurovascular Disturbances (2 papers), Hemophilia Treatment and Research (2 papers), S100 Proteins and Annexins (2 papers), Planetary Science and Exploration (2 papers) and Neuroinflammation and Neurodegeneration Mechanisms (2 papers). The work is most often cited by research in Pharmacology (99 citations), Biochemistry (56 citations), Immunology and Allergy (35 citations), Cancer Research (68 citations) and Biological Psychiatry (11 citations). Gen Kudo has collaborated with scholars based in Japan, United States and Canada. Frequent co-authors include Frank J. Gonzalez, Shioko Kimura, Hiroshi Yokota, Tian J. Yang, Ying‐Hue Lee, Masaaki Miyata, Pedro M. Fernández‐Salguero, Harry V. Gelboin, Taro E. Akiyama and Connie Cheung. Their work appears in journals such as NeuroImage, Annals of Nuclear Medicine, American Journal Of Pathology, IEEE Robotics and Automation Letters and Biochemical Pharmacology.
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.