Masashi Maekawa
- Cell Biology top 5%
- Cellular transport and secretion 10
- Microtubule and mitosis dynamics 4
- Molecular Biology top 10%
- Ubiquitin and proteasome pathways 12
- Lipid Membrane Structure and Behavior 10
- Physiology top 5%
- Erythrocyte Function and Pathophysiology 6
- Immunology and Allergy top 10%
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- Distributed systems and fault tolerance 11
- Distributed and Parallel Computing Systems 7
- Advanced Data Storage Technologies 6
- Co-authors
- Gregory D. FairnShigeki HigashiyamaTomohiko TaguchiNobukazu ArakiHiroyuki AraiYouhei EgamiAtsushi KobayashiYanbo Yang
- Journals
- Proceedings of the National Academy of Sciences (1 paper)Journal of Biological Chemistry (1 paper)Nature Communications (1 paper)
- Partner nations
- JapanCanadaUnited States
In The Last Decade
Masashi Maekawa
55 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 107
- Cell Biology 402
- Molecular Biology 863
- Physiology 52
- Aging 15
- Immunology and Allergy 49
Countries citing papers authored by Masashi Maekawa
This map shows the geographic impact of Masashi Maekawa'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 Masashi Maekawa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Masashi Maekawa more than expected).
Fields of papers citing papers by Masashi Maekawa
This network shows the impact of papers produced by Masashi Maekawa. 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 Masashi Maekawa. The network helps show where Masashi Maekawa may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Masashi Maekawa, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 5 | |
| 3 | 2024 | 2 | |
| 4 | 2023 | 8 | |
| 5 | 2023 | 3 | |
| 6 | 2022 | 9 | |
| 7 | 2021 | 12 | |
| 8 | 2020 | 14 | |
| 9 | 2020 | 7 | |
| 10 | 2019 | 4 | |
| 11 | 2019 | 24 | |
| 12 | 2019 | 10 | |
| 13 | 2018 | 44 | |
| 14 | 2017 | 79 | |
| 15 | 2017 | 12 | |
| 16 | 2014 | 88 | |
| 17 | 2014 | 86 | |
| 18 | 2014 | 112 | |
| 19 | 2012 | 10 | |
| 20 | 2009 | 14 |
About Masashi Maekawa
Masashi Maekawa is a scholar working on Aging, Cell Biology and Computer Networks and Communications, having authored 65 papers that have together received 1.2k indexed citations. Recurring topics across this work include Ubiquitin and proteasome pathways (12 papers), Distributed systems and fault tolerance (11 papers), Cellular transport and secretion (10 papers), Lipid Membrane Structure and Behavior (10 papers), Distributed and Parallel Computing Systems (7 papers), Erythrocyte Function and Pathophysiology (6 papers), Advanced Data Storage Technologies (6 papers) and Microtubule and mitosis dynamics (4 papers). The work is most often cited by research in Cell Biology (402 citations), Molecular Biology (863 citations) and Physiology (52 citations). Masashi Maekawa has collaborated with scholars based in Japan, Canada and United States. Frequent co-authors include Gregory D. Fairn, Shigeki Higashiyama, Tomohiko Taguchi, Nobukazu Araki, Hiroyuki Arai, Youhei Egami, Atsushi Kobayashi, Yanbo Yang, Katsuhisa Kawai and Tomohisa Sakaue. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Nature Communications.
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.