Akito Takamura
- Epidemiology top 0.5%
- Molecular Biology top 5%
- Cell Biology top 1%
- Physiology top 0.5%
- Pharmacology top 2%
- Co-authors
- Chieko KishiTaichi HaraNoboru MizushimaShun-ichiro IemuraTohru NatsumeJun‐Lin GuanTakeshi KaizukaNaoyuki Yamada
- Topics
- Inflammatory Myopathies and Dermatomyositis (4 papers)Rheumatoid Arthritis Research and Therapies (4 papers)Autophagy in Disease and Therapy (3 papers)
- Cited by
- PhysiologyEpidemiologyCell Biology
- Partner nations
- JapanUnited States
In The Last Decade
Akito Takamura
13 papers receiving 3.5k citations
Hit Papers
Peers
Comparison fields: 5 of 96
- Epidemiology 2.8k
- Molecular Biology 1.8k
- Cell Biology 821
- Physiology 464
- Pharmacology 365
Countries citing papers authored by Akito Takamura
This map shows the geographic impact of Akito Takamura'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 Akito Takamura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akito Takamura more than expected).
Fields of papers citing papers by Akito Takamura
This network shows the impact of papers produced by Akito Takamura. 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 Akito Takamura. The network helps show where Akito Takamura may publish in the future.
Co-authorship network of co-authors of Akito Takamura
This figure shows the co-authorship network connecting the top 25 collaborators of Akito Takamura. A scholar is included among the top collaborators of Akito Takamura 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 Akito Takamura. Akito Takamura is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 16 | |
| 3 | 13 | |
| 4 | 11 | |
| 5 | 5 | |
| 6 | 12 | |
| 7 | 11 | |
| 8 | 11 | |
| 9 | 10 | |
| 10 | Autophagy-deficient mice develop multiple liver tumorsbreakdown → | 1066 |
| 11 | Nutrient-dependent mTORC1 Association with the ULK1–Atg13–FIP200 Complex Required for Autophagybreakdown → | 1642 |
| 12 | FIP200, a ULK-interacting protein, is required for autophagosome formation in mammalian cellsbreakdown → | 783 |
| 13 | [Systematized therapeutic intervention in intrahepatic cholestasis, with special reference to ursodeoxycholic acid therapy of corticosteroid non-effective cases]. | 1 |
About Akito Takamura
Akito Takamura is a scholar working on Rheumatology, Epidemiology and Physiology, having authored 13 papers that have together received 3.6k indexed citations. Recurring topics across this work include Inflammatory Myopathies and Dermatomyositis (4 papers), Rheumatoid Arthritis Research and Therapies (4 papers) and Autophagy in Disease and Therapy (3 papers). The work is most often cited by research in Physiology (464 citations), Epidemiology (2.8k citations) and Cell Biology (821 citations). Akito Takamura has collaborated with scholars based in Japan and United States. Frequent co-authors include Chieko Kishi, Taichi Hara, Noboru Mizushima, Shun-ichiro Iemura, Tohru Natsume, Jun‐Lin Guan, Takeshi Kaizuka, Naoyuki Yamada, Noriko Oshiro and Yutaka Miura. Their work appears in journals such as Genes & Development, The Journal of Cell Biology and Molecular Biology of the Cell.
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.