Maya Kimura
Impact in
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- RNA Interference and Gene Delivery
- CRISPR and Genetic Engineering
- Advanced biosensing and bioanalysis techniques
- Pluripotent Stem Cells Research
- Muscle Physiology and Disorders
Papers in
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- Pluripotent Stem Cells Research 2
- CRISPR and Genetic Engineering 2
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- Drug-Induced Adverse Reactions 2
- Co-authors
- Hiroyuki Hozumi (1 shared paper)Masataka Ifuku (1 shared paper)Kumiko A. Iwabuchi (1 shared paper)Eriya Kenjo (1 shared paper)Naoto Inukai (1 shared paper)Yukimasa Makita (1 shared paper)Naoko Fujimoto (1 shared paper)Satoru Matsumoto (1 shared paper)
- Journals
- Biology of Reproduction (1 paper)Virology Journal (1 paper)Nature Communications (1 paper)Stem Cell Reports (1 paper)Polymer Degradation and Stability (1 paper)
- Partner nations
- JapanUnited Kingdom
In The Last Decade
Maya Kimura
9 papers receiving 345 citations
Maya Kimura's Hit Papers
Peers
Comparison fields: 5 of 79
- Molecular Biology 237
- Aging 6
- Business and International Management 6
- Condensed Matter Physics 29
- Genetics 62
Countries citing papers authored by Maya Kimura
This map shows the geographic impact of Maya Kimura'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 Maya Kimura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maya Kimura more than expected).
Fields of papers citing papers by Maya Kimura
This network shows the impact of papers produced by Maya Kimura. 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 Maya Kimura. The network helps show where Maya Kimura may publish in the future.
Co-authors
The 25 scholars most cited alongside Maya Kimura, 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 | Low immunogenicity of LNP allows repeated administrations of CRISPR-Cas9 mRNA into skeletal muscle in mice Hit paper breakdown → | 2021 | 209 |
| 2 | 2017 | 40 | |
| 3 | 2002 | 40 | |
| 4 | 2020 | 18 | |
| 5 | 2013 | 12 | |
| 6 | 2018 | 11 | |
| 7 | 2016 | 9 | |
| 8 | 2019 | 6 | |
| 9 | 2014 | 6 |
About Maya Kimura
Maya Kimura is a scholar working on Molecular Biology, Pharmacology, Cellular and Molecular Neuroscience, Cardiology and Cardiovascular Medicine and Epidemiology, having authored 9 papers that have together received 351 indexed citations. Recurring topics across this work include Pluripotent Stem Cells Research (2 papers), CRISPR and Genetic Engineering (2 papers), Drug-Induced Adverse Reactions (2 papers), Cardiac electrophysiology and arrhythmias (1 paper), Antimicrobial Resistance in Staphylococcus (1 paper), Neuroscience and Neural Engineering (1 paper), Graphene and Nanomaterials Applications (1 paper) and Urticaria and Related Conditions (1 paper). The work is most often cited by research in Molecular Biology (237 citations), Aging (6 citations), Business and International Management (6 citations), Condensed Matter Physics (29 citations) and Genetics (62 citations). Maya Kimura has collaborated with scholars based in Japan and United Kingdom. Frequent co-authors include Hiroyuki Hozumi, Masataka Ifuku, Kumiko A. Iwabuchi, Eriya Kenjo, Naoto Inukai, Yukimasa Makita, Naoko Fujimoto, Satoru Matsumoto, Akitsu Hotta and Youichi Naoe. Their work appears in journals such as Biology of Reproduction, Virology Journal, Nature Communications, Stem Cell Reports and Polymer Degradation and Stability.
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.