Taiki Abe
Impact in
- Pharmacology top 10%
- Pharmacogenetics and Drug Metabolism
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- Protein Tyrosine Phosphatases
- Ubiquitin and proteasome pathways
Papers in
-
- Protein Tyrosine Phosphatases 6
- PI3K/AKT/mTOR signaling in cancer 3
- Ubiquitin and proteasome pathways 2
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- Galectins and Cancer Biology 4
- Co-authors
- Yoko Aoki (10 shared papers)Tetsuya Niihori (10 shared papers)Kouichi Yoshinari (10 shared papers)Susumu Kodama (7 shared papers)Takuomi Hosaka (8 shared papers)Shinichi Inoue (3 shared papers)Ryota Shizu (8 shared papers)Takamitsu Sasaki (8 shared papers)
- Journals
- Blood Advances (2 papers)Cell Death and Disease (2 papers)Drug Metabolism and Pharmacokinetics (2 papers)Advanced Robotics (2 papers)iScience (1 paper)
- Partner nations
- JapanUnited StatesFrance
In The Last Decade
Taiki Abe
25 papers receiving 421 citations
Peers
Comparison fields: 5 of 75
- Pharmacology 60
- Molecular Biology 231
- Immunology 68
- Environmental Chemistry 34
- Oncology 79
Countries citing papers authored by Taiki Abe
This map shows the geographic impact of Taiki Abe'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 Taiki Abe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Taiki Abe more than expected).
Fields of papers citing papers by Taiki Abe
This network shows the impact of papers produced by Taiki Abe. 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 Taiki Abe. The network helps show where Taiki Abe may publish in the future.
Co-authors
The 25 scholars most cited alongside Taiki Abe, 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 29 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 72 | |
| 2 | 2018 | 49 | |
| 3 | 2016 | 48 | |
| 4 | 2020 | 47 | |
| 5 | 2019 | 35 | |
| 6 | 2018 | 29 | |
| 7 | 2015 | 27 | |
| 8 | 2021 | 18 | |
| 9 | 2019 | 14 | |
| 10 | 2023 | 12 | |
| 11 | 2020 | 11 | |
| 12 | 2017 | 9 | |
| 13 | 2022 | 8 | |
| 14 | 2024 | 6 | |
| 15 | 2020 | 5 | |
| 16 | 2021 | 5 | |
| 17 | 2024 | 5 | |
| 18 | 2019 | 5 | |
| 19 | 2014 | 4 | |
| 20 | 2009 | 3 |
About Taiki Abe
Taiki Abe is a scholar working on Molecular Biology, Immunology, Cell Biology, Genetics and Surgery, having authored 29 papers that have together received 424 indexed citations. Recurring topics across this work include Protein Tyrosine Phosphatases (6 papers), Galectins and Cancer Biology (4 papers), Pharmacogenetics and Drug Metabolism (3 papers), PI3K/AKT/mTOR signaling in cancer (3 papers), Endoplasmic Reticulum Stress and Disease (3 papers), Robot Manipulation and Learning (3 papers), Ubiquitin and proteasome pathways (2 papers) and Liver physiology and pathology (2 papers). The work is most often cited by research in Pharmacology (60 citations), Molecular Biology (231 citations), Immunology (68 citations), Environmental Chemistry (34 citations) and Oncology (79 citations). Taiki Abe has collaborated with scholars based in Japan, United States and France. Frequent co-authors include Yoko Aoki, Tetsuya Niihori, Kouichi Yoshinari, Susumu Kodama, Takuomi Hosaka, Shinichi Inoue, Ryota Shizu, Takamitsu Sasaki, Shin‐ichiro Kanno and Kimitoshi Yamazaki. Their work appears in journals such as Blood Advances, Cell Death and Disease, Drug Metabolism and Pharmacokinetics, Advanced Robotics and iScience.
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.