Kazuhiro Takemoto

2.7k total citations
83 papers, 1.8k citations indexed

About

Kazuhiro Takemoto is a scholar working on Molecular Biology, Atomic and Molecular Physics, and Optics and Artificial Intelligence. According to data from OpenAlex, Kazuhiro Takemoto has authored 83 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Molecular Biology, 13 papers in Atomic and Molecular Physics, and Optics and 11 papers in Artificial Intelligence. Recurrent topics in Kazuhiro Takemoto's work include Bioinformatics and Genomic Networks (20 papers), Microbial Metabolic Engineering and Bioproduction (15 papers) and Protein Structure and Dynamics (13 papers). Kazuhiro Takemoto is often cited by papers focused on Bioinformatics and Genomic Networks (20 papers), Microbial Metabolic Engineering and Bioproduction (15 papers) and Protein Structure and Dynamics (13 papers). Kazuhiro Takemoto collaborates with scholars based in Japan, United States and Australia. Kazuhiro Takemoto's co-authors include Hokuto Hirano, Tatsuya Akutsu, Hirotada Mori, Takashi Gojobori, Takeshi Itoh, Jiangning Song, Motomu Takatsu, Yoshiki Sakuma, Eiji Saitoh and Sadamichi Maekawa and has published in prestigious journals such as Applied Physics Letters, Bioinformatics and PLoS ONE.

In The Last Decade

Kazuhiro Takemoto

79 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kazuhiro Takemoto Japan 21 892 362 251 219 212 83 1.8k
Lars Folke Olsen Denmark 29 1.6k 1.7× 305 0.8× 133 0.5× 88 0.4× 111 0.5× 120 3.4k
Paul E. Anderson United States 27 709 0.8× 86 0.2× 126 0.5× 142 0.6× 216 1.0× 115 2.4k
Ulrich Gerland Germany 33 3.2k 3.6× 426 1.2× 169 0.7× 98 0.4× 259 1.2× 90 4.2k
Eivind Almaas Norway 24 1.5k 1.7× 218 0.6× 50 0.2× 113 0.5× 89 0.4× 73 2.6k
Kay Hamacher Germany 21 952 1.1× 104 0.3× 45 0.2× 235 1.1× 72 0.3× 102 1.8k
Klaus Schulten United States 19 919 1.0× 449 1.2× 77 0.3× 82 0.4× 100 0.5× 33 1.5k
Francesco Rao Germany 26 2.2k 2.5× 393 1.1× 41 0.2× 49 0.2× 117 0.6× 48 2.8k
Deryck J. Mills Germany 35 3.1k 3.5× 158 0.4× 117 0.5× 78 0.4× 199 0.9× 76 3.9k
José M. G. Vilar Spain 25 1.9k 2.1× 238 0.7× 39 0.2× 41 0.2× 174 0.8× 60 3.1k
Alexandre V. Morozov United States 29 2.4k 2.7× 124 0.3× 151 0.6× 40 0.2× 59 0.3× 67 2.9k

Countries citing papers authored by Kazuhiro Takemoto

Since Specialization
Citations

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

Fields of papers citing papers by Kazuhiro Takemoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kazuhiro Takemoto

This figure shows the co-authorship network connecting the top 25 collaborators of Kazuhiro Takemoto. A scholar is included among the top collaborators of Kazuhiro Takemoto 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 Kazuhiro Takemoto. Kazuhiro Takemoto 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.
Oda, Jun & Kazuhiro Takemoto. (2025). Mobile applications for skin cancer detection are vulnerable to physical camera-based adversarial attacks. Scientific Reports. 15(1). 18119–18119. 1 indexed citations
2.
Takemoto, Kazuhiro. (2024). The moral machine experiment on large language models. Royal Society Open Science. 11(2). 17 indexed citations
3.
Takemoto, Kazuhiro. (2024). All in How You Ask for It: Simple Black-Box Method for Jailbreak Attacks. Applied Sciences. 14(9). 3558–3558. 3 indexed citations
4.
Takemoto, Kazuhiro, et al.. (2023). Mitigation of adversarial attacks on voter model dynamics by network heterogeneity. Journal of Physics Complexity. 4(2). 25009–25009. 3 indexed citations
5.
Matsuo, Yuki & Kazuhiro Takemoto. (2022). Backdoor Attacks on Deep Neural Networks via Transfer Learning from Natural Images. Applied Sciences. 12(24). 12564–12564. 6 indexed citations
6.
Hirano, Hokuto, et al.. (2021). Universal adversarial attacks on deep neural networks for medical image classification. BMC Medical Imaging. 21(1). 9–9. 91 indexed citations
7.
Ueda, Issei, Kazuhiro Takemoto, Keita Watanabe, et al.. (2020). The brain-derived neurotrophic factor Val66Met polymorphism increases segregation of structural correlation networks in healthy adult brains. PeerJ. 8. e9632–e9632. 5 indexed citations
8.
Takemoto, Kazuhiro, et al.. (2018). Network resilience of mutualistic ecosystems and environmental changes: an empirical study. Royal Society Open Science. 5(9). 180706–180706. 11 indexed citations
9.
Takemoto, Kazuhiro, et al.. (2017). Exosomes in mammals with greater habitat variability contain more proteins and RNAs. Royal Society Open Science. 4(4). 170162–170162. 3 indexed citations
10.
Takami, Hideto, et al.. (2016). An automated system for evaluation of the potential functionome: MAPLE version 2.1.0. DNA Research. 23(5). 467–475. 44 indexed citations
11.
Takemoto, Kazuhiro. (2015). Habitat variability does not generally promote metabolic network modularity in flies and mammals. Biosystems. 139. 46–54. 3 indexed citations
12.
Takemoto, Kazuhiro. (2015). Heterogeneity of cells may explain allometric scaling of metabolic rate. Biosystems. 130. 11–16. 6 indexed citations
13.
Takemoto, Kazuhiro. (2013). Correction: Does Habitat Variability Really Promote Metabolic Network Modularity?. PLoS ONE. 8(10). 5 indexed citations
14.
Takemoto, Kazuhiro. (2013). Does Habitat Variability Really Promote Metabolic Network Modularity?. PLoS ONE. 8(4). e61348–e61348. 15 indexed citations
15.
Takemoto, Kazuhiro, et al.. (2013). Modular organization of cancer signaling networks is associated with patient survivability. Biosystems. 113(3). 149–154. 16 indexed citations
16.
Wang, Mingjun, Xing‐Ming Zhao, Kazuhiro Takemoto, et al.. (2012). FunSAV: Predicting the Functional Effect of Single Amino Acid Variants Using a Two-Stage Random Forest Model. PLoS ONE. 7(8). e43847–e43847. 41 indexed citations
17.
Takemoto, Kazuhiro. (2012). Metabolic network modularity arising from simple growth processes. Physical Review E. 86(3). 36107–36107. 12 indexed citations
18.
Tamura, Takeyuki, Kazuhiro Takemoto, & Tatsuya Akutsu. (2010). Finding Minimum Reaction Cuts of Metabolic Networks Under a Boolean Model Using Integer Programming and Feedback Vertex Sets. RePEc: Research Papers in Economics. 1(1). 14–31. 18 indexed citations
19.
Takemoto, Kazuhiro, Jose C. Nacher, & Tatsuya Akutsu. (2007). Correlation between structure and temperature in prokaryotic metabolic networks. BMC Bioinformatics. 8(1). 303–303. 33 indexed citations
20.
Takemoto, Kazuhiro, et al.. (2006). Modeling for evolving biological networks with scale-free connectivity, hierarchical modularity, and disassortativity. Mathematical Biosciences. 208(2). 454–468. 17 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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