Daigo Takemoto

4.4k total citations
85 papers, 3.2k citations indexed

About

Daigo Takemoto is a scholar working on Plant Science, Molecular Biology and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Daigo Takemoto has authored 85 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 67 papers in Plant Science, 35 papers in Molecular Biology and 22 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Daigo Takemoto's work include Plant-Microbe Interactions and Immunity (43 papers), Plant and fungal interactions (19 papers) and Plant Pathogens and Resistance (16 papers). Daigo Takemoto is often cited by papers focused on Plant-Microbe Interactions and Immunity (43 papers), Plant and fungal interactions (19 papers) and Plant Pathogens and Resistance (16 papers). Daigo Takemoto collaborates with scholars based in Japan, New Zealand and Australia. Daigo Takemoto's co-authors include David A. Jones, Aiko Tanaka, Barry Scott, Adrienne R. Hardham, Kazuhito Kawakita, Pyoyun Park, Yusuke Shibata, Michael J. Christensen, Noriyuki Doke and Makoto Ojika and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and The Plant Cell.

In The Last Decade

Daigo Takemoto

83 papers receiving 3.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daigo Takemoto Japan 30 2.4k 1.2k 602 586 423 85 3.2k
Aiko Tanaka Japan 22 1.6k 0.7× 963 0.8× 958 1.6× 677 1.2× 499 1.2× 62 2.5k
Philippe Silar France 31 1.5k 0.6× 1.9k 1.5× 193 0.3× 542 0.9× 537 1.3× 108 3.0k
Makoto Fujimura Japan 27 1.9k 0.8× 1.2k 0.9× 713 1.2× 903 1.5× 430 1.0× 63 2.5k
Stefanie Pöggeler Germany 38 1.8k 0.7× 2.4k 2.0× 227 0.4× 1.0k 1.7× 1.0k 2.4× 86 3.4k
Lynda M. Ciuffetti United States 30 2.7k 1.1× 879 0.7× 230 0.4× 992 1.7× 138 0.3× 53 3.2k
David G. Gilchrist United States 30 2.3k 1.0× 1.2k 1.0× 460 0.8× 556 0.9× 119 0.3× 59 3.0k
James A. Sweigard United States 24 2.7k 1.1× 2.3k 1.8× 225 0.4× 1.4k 2.4× 549 1.3× 38 3.6k
Frances Trail United States 35 3.3k 1.4× 1.5k 1.2× 325 0.5× 2.0k 3.4× 722 1.7× 73 4.0k
Barry Scott New Zealand 40 2.1k 0.9× 2.2k 1.7× 2.4k 4.0× 1.1k 1.8× 1.7k 4.1× 95 4.8k
Tomonori Shiraishi Japan 33 3.0k 1.3× 1.3k 1.1× 161 0.3× 561 1.0× 103 0.2× 166 3.7k

Countries citing papers authored by Daigo Takemoto

Since Specialization
Citations

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

Fields of papers citing papers by Daigo Takemoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daigo Takemoto

This figure shows the co-authorship network connecting the top 25 collaborators of Daigo Takemoto. A scholar is included among the top collaborators of Daigo 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 Daigo Takemoto. Daigo 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.
Sato, Ikuo, et al.. (2025). Detection, isolation, and identification of Rhizoctonia theobromae associated with cassava witches’ broom disease in the Philippines. Physiological and Molecular Plant Pathology. 140. 102872–102872.
2.
Kato, Hiroaki, Kentaro Matsuda, Atsushi Miura, et al.. (2024). Two structurally different oomycete lipophilic microbe-associated molecular patterns induce distinctive plant immune responses. PLANT PHYSIOLOGY. 196(1). 479–494. 4 indexed citations
3.
4.
Miura, Atsushi, Maurizio Camagna, Aiko Tanaka, et al.. (2024). Botrytis cinerea detoxifies the sesquiterpenoid phytoalexin rishitin through multiple metabolizing pathways. Fungal Genetics and Biology. 172. 103895–103895. 2 indexed citations
5.
Camagna, Maurizio, et al.. (2023). Leaf blight of rice-paper plant, Tetrapanax papyrifer, caused by Neofusicoccum parvum: a potential source of stem rot diseases of mango and grape. Journal of General Plant Pathology. 89(3). 179–184. 1 indexed citations
6.
Kato, Hiroaki, Maurizio Camagna, Aiko Tanaka, et al.. (2023). Induction of plant disease resistance by mixed oligosaccharide elicitors prepared from plant cell wall and crustacean shells. Physiologia Plantarum. 175(5). e14052–e14052. 18 indexed citations
7.
8.
Camagna, Maurizio, Aiko Tanaka, Ikuo Sato, et al.. (2023). Botrytis cinerea tolerates phytoalexins produced by Solanaceae and Fabaceae plants through an efflux transporter BcatrB and metabolizing enzymes. Frontiers in Plant Science. 14. 1177060–1177060. 16 indexed citations
9.
Suzuki, Takamasa, Aiko Tanaka, Maurizio Camagna, et al.. (2022). Botrytis cinerea identifies host plants via the recognition of antifungal capsidiol to induce expression of a specific detoxification gene. PNAS Nexus. 1(5). pgac274–pgac274. 20 indexed citations
10.
Kato, Hiroaki, Keiichirou Nemoto, Motoki Shimizu, et al.. (2022). Recognition of pathogen-derived sphingolipids in Arabidopsis. Science. 376(6595). 857–860. 31 indexed citations
11.
Salaipeth, Lakha, Subha Das, Hideki Kondō, et al.. (2021). Omnipresence of Partitiviruses in Rice Aggregate Sheath Spot Symptom-Associated Fungal Isolates from Paddies in Thailand. Viruses. 13(11). 2269–2269. 6 indexed citations
12.
13.
Hillier, Shawn, David Newsome, Daigo Takemoto, et al.. (2016). Preclinical characterization of the selective DNA-dependent protein kinase (DNA-PK) inhibitor VX-984 in combination with chemotherapy. Annals of Oncology. 27. vi122–vi122. 4 indexed citations
14.
Wada, Shinya, et al.. (2013). NON-HOST RESISTANCE ACTIVITIES OF Arabidopsis thaliana INDUCED BY METHANOL EXTRACT OF MYCELIA FROM Phytophthora infestans. International Journal of Biosciences. 1(2). 2 indexed citations
15.
16.
Ito, Hiroaki, Masaki Ochiai, Hiroaki Kato, et al.. (2012). Rose Phytoene Desaturase Gene Silencing by Apple Latent Spherical Virus Vectors. HortScience. 47(9). 1278–1282. 7 indexed citations
17.
Scott, Barry, Daigo Takemoto, & Aiko Tanaka. (2007). Fungal Endophyte Production of Reactive Oxygen Species is Critical for Maintaining the Mutualistic Symbiotic Interaction BetweenEpichloë festucaeand Perennial Ryegrass. Plant Signaling & Behavior. 2(3). 171–173. 17 indexed citations
18.
Tanaka, Aiko, Michael J. Christensen, Daigo Takemoto, Pyoyun Park, & Barry Scott. (2006). Reactive Oxygen Species Play a Role in Regulating a Fungus–Perennial Ryegrass Mutualistic Interaction. The Plant Cell. 18(4). 1052–1066. 354 indexed citations
19.
Takemoto, Daigo & David A. Jones. (2005). Membrane Release and Destabilization of Arabidopsis RIN4 Following Cleavage by Pseudomonas syringae AvrRpt2. Molecular Plant-Microbe Interactions. 18(12). 1258–1268. 52 indexed citations
20.
Takemoto, Daigo, et al.. (1997). Identification of Chitinase and Osmotin-Like Protein as Actin-Binding Proteins in Suspension-Cultured Potato Cells. Plant and Cell Physiology. 38(4). 441–448. 50 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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