Tsuyoshi Hamada

1.7k total citations · 1 hit paper
36 papers, 1.0k citations indexed

About

Tsuyoshi Hamada is a scholar working on Molecular Biology, Statistical and Nonlinear Physics and Artificial Intelligence. According to data from OpenAlex, Tsuyoshi Hamada has authored 36 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 8 papers in Statistical and Nonlinear Physics and 6 papers in Artificial Intelligence. Recurrent topics in Tsuyoshi Hamada's work include Scientific Research and Discoveries (7 papers), Protein Structure and Dynamics (4 papers) and Electromagnetic Scattering and Analysis (3 papers). Tsuyoshi Hamada is often cited by papers focused on Scientific Research and Discoveries (7 papers), Protein Structure and Dynamics (4 papers) and Electromagnetic Scattering and Analysis (3 papers). Tsuyoshi Hamada collaborates with scholars based in Japan, United Kingdom and China. Tsuyoshi Hamada's co-authors include Yoshio Ishiguro, Yoshiki Ninomiya, Kazuya Takeda, Eijiro Takeuchi, Shinpei Kato, Keigo Nitadori, Rio Yokota, Toru TAKAHASHI, Makoto Taiji and Kenji Yasuoka and has published in prestigious journals such as Nature Immunology, Gastroenterology and PLoS ONE.

In The Last Decade

Tsuyoshi Hamada

34 papers receiving 972 citations

Hit Papers

An Open Approach to Autonomous Vehicles 2015 2026 2018 2022 2015 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Tsuyoshi Hamada Japan 15 202 179 153 149 147 36 1.0k
Jeyan Thiyagalingam United Kingdom 19 152 0.8× 140 0.8× 44 0.3× 246 1.7× 122 0.8× 66 1.1k
David E. Stewart United States 24 200 1.0× 180 1.0× 144 0.9× 104 0.7× 143 1.0× 84 2.8k
Yinghui Gao China 18 87 0.4× 445 2.5× 36 0.2× 292 2.0× 171 1.2× 108 1.6k
Takashi Maekawa Japan 30 188 0.9× 579 3.2× 83 0.5× 69 0.5× 124 0.8× 81 2.7k
Cheng Li China 20 38 0.2× 598 3.3× 163 1.1× 147 1.0× 253 1.7× 81 1.7k
Thomas Johansson Sweden 26 76 0.4× 653 3.6× 193 1.3× 456 3.1× 45 0.3× 150 2.4k
Wei Huang China 22 27 0.1× 58 0.3× 116 0.8× 913 6.1× 82 0.6× 160 1.6k
Sean B. Andersson United States 22 20 0.1× 217 1.2× 83 0.5× 302 2.0× 98 0.7× 155 1.7k
Andrea Boni Italy 24 17 0.1× 176 1.0× 238 1.6× 1.0k 6.9× 70 0.5× 161 2.3k

Countries citing papers authored by Tsuyoshi Hamada

Since Specialization
Citations

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

Fields of papers citing papers by Tsuyoshi Hamada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tsuyoshi Hamada

This figure shows the co-authorship network connecting the top 25 collaborators of Tsuyoshi Hamada. A scholar is included among the top collaborators of Tsuyoshi Hamada 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 Tsuyoshi Hamada. Tsuyoshi Hamada 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.
Hamada, Tsuyoshi, Nobuaki Michihata, Tomotaka Saito, et al.. (2023). Inverse association of hospital volume with in-hospital mortality rate of patients receiving EUS-guided interventions for pancreatic fluid collections. Gastrointestinal Endoscopy. 98(4). 597–606.e2. 5 indexed citations
2.
Saito, Kei, Yousuke Nakai, Kazunaga Ishigaki, et al.. (2022). P21-17 Safety and effectiveness of mirogabalin for CIPN induced by nab-paclitaxel in patients with pancreatic cancer. Annals of Oncology. 33. S520–S520.
3.
Ishibashi, Daisuke, Takeshi Ishikawa, Satoshi Mizuta, et al.. (2020). Novel Compounds Identified by Structure-Based Prion Disease Drug Discovery Using In Silico Screening Delay the Progression of an Illness in Prion-Infected Mice. Neurotherapeutics. 17(4). 1836–1849. 2 indexed citations
4.
5.
Watanabe, Ken, Takeshi Ishikawa, Satoshi Mizuta, et al.. (2017). Identification of small molecule inhibitors for influenza a virus using in silico and in vitro approaches. PLoS ONE. 12(3). e0173582–e0173582. 23 indexed citations
6.
Watanabe, Ken, Takeshi Ishikawa, Satoshi Mizuta, et al.. (2017). Structure-based drug discovery for combating influenza virus by targeting the PA–PB1 interaction. Scientific Reports. 7(1). 9500–9500. 34 indexed citations
7.
Ishibashi, Daisuke, Takehiro Nakagaki, Takeshi Ishikawa, et al.. (2016). Structure-Based Drug Discovery for Prion Disease Using a Novel Binding Simulation. EBioMedicine. 9. 238–249. 35 indexed citations
8.
Omotuyi, Olaposi Idowu & Tsuyoshi Hamada. (2015). Theoretical Dynamics and Energetics of HLA-A2/SLYNTVATL Interaction. 5(1). 1–8. 1 indexed citations
9.
Omotuyi, Olaposi Idowu & Tsuyoshi Hamada. (2015). Human furin Cys198 imposes dihedral and positional restraints on His194 for optimal Ser386-proton transfer. Journal of Biomolecular Structure and Dynamics. 33(11). 2442–2451. 1 indexed citations
10.
Kato, Shinpei, Eijiro Takeuchi, Yoshio Ishiguro, et al.. (2015). An Open Approach to Autonomous Vehicles. IEEE Micro. 35(6). 60–68. 391 indexed citations breakdown →
11.
Ling, Cheng, et al.. (2013). MrBayes tgMC3: A Tight GPU Implementation of MrBayes. PLoS ONE. 8(4). e60667–e60667. 8 indexed citations
12.
Berczik, Peter, Keigo Nitadori, Rainer Spurzem, et al.. (2011). High performance massively parallel direct N-body simulations on large GPU clusters. 8–18. 9 indexed citations
13.
Shibata, Yuichiro, et al.. (2010). Implementation of a programming environment with a multithread model for reconfigurable systems. ACM SIGARCH Computer Architecture News. 38(4). 40–45.
14.
Yokota, Rio, Jaydeep P. Bardhan, Matthew G. Knepley, Lorena A. Barba, & Tsuyoshi Hamada. (2010). Biomolecular electrostatics simulation with a parallel FMM-based BEM, using up to 512 GPUs. 1 indexed citations
15.
Yokota, Rio, Tsuyoshi Hamada, Jaydeep P. Bardhan, Matthew G. Knepley, & Lorena A. Barba. (2010). Biomolecular Electrostatics Simulation by an FMM-based BEM on 512 GPUs. 1 indexed citations
16.
Shibata, Yuichiro, et al.. (2009). FPGA implementation and accuracy evaluation of a power-supply voltage control circuit. IEICE technical report. Speech. 109(198). 19–24. 2 indexed citations
17.
Hamada, Tsuyoshi, et al.. (2009). Accelerating collapsed variational bayesian inference for latent dirichlet allocation with nvidia CUDA compatible devices. Lecture notes in computer science. 5579. 491–500. 1 indexed citations
18.
TAKAHASHI, Toru & Tsuyoshi Hamada. (2009). GPU‐accelerated boundary element method for Helmholtz' equation in three dimensions. International Journal for Numerical Methods in Engineering. 80(10). 1295–1321. 52 indexed citations
19.
Cheng, Ling, Khaled Benkrid, & Tsuyoshi Hamada. (2009). A parameterisable and scalable Smith-Waterman algorithm implementation on CUDA-compatible GPUs. Nagasaki University's Academic Output SITE (Nagasaki University). 94–100. 15 indexed citations
20.
Terashima, Yuya, Nobuyuki Onai, Masako Murai, et al.. (2005). Pivotal function for cytoplasmic protein FROUNT in CCR2-mediated monocyte chemotaxis. Nature Immunology. 6(8). 827–835. 93 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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