Takeshi Noda
- Physiology top 0.02%
- Lysosomal Storage Disorders Research 11
- Epidemiology top 0.02%
- Autophagy in Disease and Therapy 75
- Cell Biology top 0.02%
- Endoplasmic Reticulum Stress and Disease 41
- Cellular transport and secretion 26
- Parasitology top 0.2%
- Aging top 0.5%
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- Fusion materials and technologies 31
- Nuclear Materials and Properties 15
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- Advanced ceramic materials synthesis 17
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- Microstructure and Mechanical Properties of Steels 16
- Co-authors
- Tamotsu YoshimoriYoshinori OhsumiNaonobu FujitaShunsuke KimuraHiroko OmoriNoboru MizushimaNaotada IshiharaMariko Ohsumi
- Cited by
- PhysiologyEpidemiologyCell Biology
- Journals
- Nature (4 papers)Proceedings of the National Academy of Sciences (2 papers)Advanced Materials (1 paper)
- Partner nations
- JapanUnited StatesChina
In The Last Decade
Takeshi Noda
185 papers receiving 25.6k citations
Hit Papers
Peers
Comparison fields: 5 of 178
- Physiology 2.9k
- Epidemiology 17.4k
- Cell Biology 7.8k
- Parasitology 1.7k
- Aging 326
Countries citing papers authored by Takeshi Noda
This map shows the geographic impact of Takeshi Noda'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 Takeshi Noda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takeshi Noda more than expected).
Fields of papers citing papers by Takeshi Noda
This network shows the impact of papers produced by Takeshi Noda. 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 Takeshi Noda. The network helps show where Takeshi Noda may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Takeshi Noda, 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 | 2024 | 1 | |
| 2 | 2023 | 6 | |
| 3 | 2023 | 3 | |
| 4 | 2023 | 8 | |
| 5 | 2021 | 12 | |
| 6 | 2020 | 21 | |
| 7 | 2017 | 62 | |
| 8 | 2016 | 12 | |
| 9 | 2015 | 55 | |
| 10 | 2015 | 9 | |
| 11 | 2013 | 226 | |
| 12 | 2011 | 144 | |
| 13 | 2010 | 122 | |
| 14 | Atg9a controls dsDNA-driven dynamic translocation of STING and the innate immune responsebreakdown → | 2009 | 675 |
| 15 | The Atg16L Complex Specifies the Site of LC3 Lipidation for Membrane Biogenesis in Autophagybreakdown → | 2008 | 847 |
| 16 | 2008 | 117 | |
| 17 | 2004 | 29 | |
| 18 | 1998 | 51 | |
| 19 | 1997 | 136 | |
| 20 | 1990 | 65 |
About Takeshi Noda
Takeshi Noda is a scholar working on Cell Biology, Physiology and Ceramics and Composites, having authored 186 papers that have together received 26.0k indexed citations. Recurring topics across this work include Autophagy in Disease and Therapy (75 papers), Endoplasmic Reticulum Stress and Disease (41 papers), Fusion materials and technologies (31 papers), Cellular transport and secretion (26 papers), Advanced ceramic materials synthesis (17 papers), Microstructure and Mechanical Properties of Steels (16 papers), Nuclear Materials and Properties (15 papers) and Lysosomal Storage Disorders Research (11 papers). The work is most often cited by research in Physiology (2.9k citations), Epidemiology (17.4k citations) and Cell Biology (7.8k citations). Takeshi Noda has collaborated with scholars based in Japan, United States and China. Frequent co-authors include Tamotsu Yoshimori, Yoshinori Ohsumi, Naonobu Fujita, Shunsuke Kimura, Hiroko Omori, Noboru Mizushima, Naotada Ishihara, Mariko Ohsumi, Akitsugu Yamamoto and Tatsuya Saitoh. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Advanced Materials.
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.