Satoshi Miyamoto

2.7k total citations
76 papers, 1.5k citations indexed

About

Satoshi Miyamoto is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Satoshi Miyamoto has authored 76 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 15 papers in Molecular Biology and 15 papers in Computer Vision and Pattern Recognition. Recurrent topics in Satoshi Miyamoto's work include Face and Expression Recognition (11 papers), Advanced Clustering Algorithms Research (11 papers) and Rough Sets and Fuzzy Logic (10 papers). Satoshi Miyamoto is often cited by papers focused on Face and Expression Recognition (11 papers), Advanced Clustering Algorithms Research (11 papers) and Rough Sets and Fuzzy Logic (10 papers). Satoshi Miyamoto collaborates with scholars based in Japan, United States and Russia. Satoshi Miyamoto's co-authors include Kenichi Shikata, Kumar Sharma, Hirofumi Makino, Ryo Kodera, Nobuo Kajitani, Daisho Hirota, Motofumi Sasaki, Chikage Sato, Hitomi Kataoka and Daisuke Ogawa and has published in prestigious journals such as Journal of Biological Chemistry, Blood and IEEE Transactions on Automatic Control.

In The Last Decade

Satoshi Miyamoto

70 papers receiving 1.4k citations

Peers

Satoshi Miyamoto
Guy Lemieux Canada
Amit Gupta India
Jason Flannick United States
Junying Huang China
Minghao Ye United States
Qi Feng China
Kunlun He China
Jianwen Chen China
Felix Agakov United Kingdom
Juan Manuel Moreno Spain
Guy Lemieux Canada
Satoshi Miyamoto
Citations per year, relative to Satoshi Miyamoto Satoshi Miyamoto (= 1×) peers Guy Lemieux

Countries citing papers authored by Satoshi Miyamoto

Since Specialization
Citations

This map shows the geographic impact of Satoshi Miyamoto'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 Satoshi Miyamoto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Satoshi Miyamoto more than expected).

Fields of papers citing papers by Satoshi Miyamoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Satoshi Miyamoto. 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 Satoshi Miyamoto. The network helps show where Satoshi Miyamoto may publish in the future.

Co-authorship network of co-authors of Satoshi Miyamoto

This figure shows the co-authorship network connecting the top 25 collaborators of Satoshi Miyamoto. A scholar is included among the top collaborators of Satoshi Miyamoto 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 Satoshi Miyamoto. Satoshi Miyamoto is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hashimoto, Kazuki, Yuko Abe, Kiyoharu Fukushima, et al.. (2024). Epidemiology of bronchiectasis at a single center in Japan: a retrospective cohort study. BMC Pulmonary Medicine. 24(1). 531–531.
2.
Hashimoto, Kazuki, Takuro Nii, M. Yokoyama, et al.. (2023). Diagnosis and Management of Drug-Induced Interstitial Lung Disease Associated with Amikacin Liposome Inhalation Suspension in Refractory Mycobacterium Avium Complex Pulmonary Disease: A Case Report. Infection and Drug Resistance. Volume 16. 6629–6634. 2 indexed citations
3.
Miyamoto, Satoshi, et al.. (2022). Suramin prevents the development of diabetic kidney disease by inhibiting NLRP3 inflammasome activation in KK‐Ay mice. Journal of Diabetes Investigation. 14(2). 205–220. 9 indexed citations
4.
5.
Sera, Toshihiro, et al.. (2020). A metabolic reaction–diffusion model for PKCα translocation via PIP2 hydrolysis in an endothelial cell. Biochemical Journal. 477(20). 4071–4084. 2 indexed citations
6.
Shikata, Kenichi, Masakazu Haneda, Toshiharu Ninomiya, et al.. (2020). Randomized trial of an intensified, multifactorial intervention in patients with advanced‐stage diabetic kidney disease: Diabetic Nephropathy Remission and Regression Team Trial in Japan (DNETT‐Japan). Journal of Diabetes Investigation. 12(2). 207–216. 17 indexed citations
7.
Miyamoto, Satoshi, Guanshi Zhang, David L. Hall, et al.. (2020). Restoring mitochondrial superoxide levels with elamipretide (MTP-131) protects db/db mice against progression of diabetic kidney disease. Journal of Biological Chemistry. 295(21). 7249–7260. 36 indexed citations
8.
Eikawa, Shingo, Nahoko Tomonobu, Nobuo Kajitani, et al.. (2020). Dysfunction of CD8 + PD-1 + T cells in type 2 diabetes caused by the impairment of metabolism-immune axis. Scientific Reports. 10(1). 14928–14928. 38 indexed citations
9.
Shikata, Kenichi, Ryo Kodera, Kazunori Utsunomiya, et al.. (2019). Prevalence of albuminuria and renal dysfunction, and related clinical factors in Japanese patients with diabetes: The Japan Diabetes Complication and its Prevention prospective study 5. Journal of Diabetes Investigation. 11(2). 325–332. 17 indexed citations
10.
Katayama, Akihiro, Atsuhito Tone, Mayu Watanabe, et al.. (2019). The hypoglycemia-prevention effect of sensor-augmented pump therapy with predictive low glucose management in Japanese patients with type 1 diabetes mellitus: a short-term study. Diabetology International. 11(2). 97–104. 7 indexed citations
11.
Oka, Hiromasa, Kazuhiro Ikegai, Shohei Shirakami, et al.. (2018). Design, synthesis, and biological evaluation of novel biphenyl-4-carboxamide derivatives as orally available TRPV1 antagonists. Bioorganic & Medicinal Chemistry. 26(12). 3716–3726. 5 indexed citations
12.
Miyamoto, Satoshi, Cheng‐Chih Hsu, Grégory Hamm, et al.. (2016). Mass Spectrometry Imaging Reveals Elevated Glomerular ATP/AMP in Diabetes/obesity and Identifies Sphingomyelin as a Possible Mediator. EBioMedicine. 7. 121–134. 103 indexed citations
13.
Masuda, Naoyuki, Satoshi Miyamoto, Takuma Mihara, et al.. (2014). Synthesis, SAR study, and biological evaluation of novel quinoline derivatives as phosphodiesterase 10A inhibitors with reduced CYP3A4 inhibition. Bioorganic & Medicinal Chemistry. 23(2). 297–313. 19 indexed citations
14.
Miyamoto, Satoshi & Kumar Sharma. (2013). Adipokines protecting CKD. Nephrology Dialysis Transplantation. 28(suppl 4). iv15–iv22. 15 indexed citations
15.
Kodera, Ryo, Kenichi Shikata, Satoshi Miyamoto, et al.. (2013). Dipeptidyl peptidase-4 inhibitor ameliorates early renal injury through its anti-inflammatory action in a rat model of type 1 diabetes. Biochemical and Biophysical Research Communications. 443(3). 828–833. 84 indexed citations
16.
Masuda, Naoyuki, Satoshi Miyamoto, Y. Amano, et al.. (2013). Design and synthesis of novel benzimidazole derivatives as phosphodiesterase 10A inhibitors with reduced CYP1A2 inhibition. Bioorganic & Medicinal Chemistry. 21(24). 7612–7623. 29 indexed citations
17.
Miyamoto, Satoshi, Kenichi Shikata, Kyoko Miyasaka, et al.. (2012). Cholecystokinin Plays a Novel Protective Role in Diabetic Kidney Through Anti-inflammatory Actions on Macrophage. Diabetes. 61(4). 897–907. 60 indexed citations
18.
Nakamura, Taro, et al.. (2006). Kernelized Cluster Validity Measures and Application to Evaluation of Different Clustering Algorithms. 7. 763–769. 3 indexed citations
19.
Miyamoto, Satoshi. (2002). Fuzzy multisets and fuzzy clustering of documents. 2. 1539–1542. 16 indexed citations
20.
Ogawa, Takumi, Kaoru Kobayashi, Satoshi Miyamoto, et al.. (1996). A clinical study on indication of pumping manipulation and subsequent occlusal management. 8(1). 50–61. 7 indexed citations

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.

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