Yoji Kawano

5.4k total citations · 1 hit paper
47 papers, 4.4k citations indexed

About

Yoji Kawano is a scholar working on Plant Science, Molecular Biology and Cell Biology. According to data from OpenAlex, Yoji Kawano has authored 47 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Plant Science, 19 papers in Molecular Biology and 12 papers in Cell Biology. Recurrent topics in Yoji Kawano's work include Plant-Microbe Interactions and Immunity (24 papers), Legume Nitrogen Fixing Symbiosis (16 papers) and Plant Pathogenic Bacteria Studies (11 papers). Yoji Kawano is often cited by papers focused on Plant-Microbe Interactions and Immunity (24 papers), Legume Nitrogen Fixing Symbiosis (16 papers) and Plant Pathogenic Bacteria Studies (11 papers). Yoji Kawano collaborates with scholars based in Japan, China and United States. Yoji Kawano's co-authors include Kozo Kaibuchi, Nariko Arimura, Takeshi Yoshimura, Saeko Kawabata, Ko Shimamoto, Mutsuki Amano, Akira Kikuchi, Yuko Fukata, Noriko Oshiro and Masaki Inagaki and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Yoji Kawano

46 papers receiving 4.4k citations

Hit Papers

GSK-3β Regulates Phosphor... 2005 2026 2012 2019 2005 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yoji Kawano Japan 27 2.3k 1.3k 1.2k 1.2k 396 47 4.4k
Jack Roos United States 23 3.2k 1.4× 468 0.4× 2.1k 1.7× 1.5k 1.2× 64 0.2× 36 6.0k
Joshua S. Kaminker United States 27 2.7k 1.2× 564 0.4× 603 0.5× 292 0.2× 229 0.6× 36 4.3k
Elke Stein Germany 24 2.0k 0.9× 924 0.7× 1.9k 1.5× 985 0.8× 655 1.7× 30 3.7k
Tamara C. Petrucci Italy 34 2.5k 1.1× 261 0.2× 611 0.5× 822 0.7× 82 0.2× 84 3.4k
Jeannette Kunz Kazakhstan 24 3.0k 1.3× 297 0.2× 402 0.3× 1.2k 0.9× 96 0.2× 40 4.0k
Lorene K. Langeberg United States 45 6.5k 2.9× 343 0.3× 2.1k 1.7× 1.4k 1.1× 120 0.3× 73 8.2k
Elena I. Rugarli Germany 39 4.4k 1.9× 134 0.1× 1.1k 0.9× 791 0.6× 168 0.4× 80 5.8k
Bruce A. Hamilton United States 29 2.0k 0.9× 204 0.2× 622 0.5× 434 0.4× 110 0.3× 69 3.5k
Xiaohang Yang China 29 2.0k 0.9× 300 0.2× 681 0.5× 895 0.7× 101 0.3× 103 2.8k

Countries citing papers authored by Yoji Kawano

Since Specialization
Citations

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

Fields of papers citing papers by Yoji Kawano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yoji Kawano

This figure shows the co-authorship network connecting the top 25 collaborators of Yoji Kawano. A scholar is included among the top collaborators of Yoji Kawano 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 Yoji Kawano. Yoji Kawano 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
2.
Wang, Pingyu, Huimin Jia, Ting Guo, et al.. (2022). The secreted immune response peptide 1 functions as a phytocytokine in rice immunity. Journal of Experimental Botany. 74(3). 1059–1073. 8 indexed citations
3.
Wang, Qiong & Yoji Kawano. (2022). Improving disease resistance to rice false smut without yield penalty by manipulating the expression of effector target. Molecular Plant. 15(12). 1834–1837. 4 indexed citations
4.
Akamatsu, Akira, Masayuki Fujiwara, Satoshi Hamada, et al.. (2021). The Small GTPase OsRac1 Forms Two Distinct Immune Receptor Complexes Containing the PRR OsCERK1 and the NLR Pit. Plant and Cell Physiology. 62(11). 1662–1675. 6 indexed citations
5.
Xie, Yongyao, Yaling Zhang, Jingluan Han, et al.. (2018). The Intronic cis Element SE1 Recruits trans-Acting Repressor Complexes to Repress the Expression of ELONGATED UPPERMOST INTERNODE1 in Rice. Molecular Plant. 11(5). 720–735. 53 indexed citations
6.
Nagano, Minoru, Toshiki Ishikawa, Masayuki Fujiwara, et al.. (2016). Plasma Membrane Microdomains Are Essential for Rac1-RbohB/H-Mediated Immunity in Rice. The Plant Cell. 28(8). 1966–1983. 114 indexed citations
7.
Akamatsu, Akira, Ko Shimamoto, & Yoji Kawano. (2016). Crosstalk of Signaling Mechanisms Involved in Host Defense and Symbiosis Against Microorganisms in Rice. Current Genomics. 17(4). 297–307. 12 indexed citations
8.
Liu, Jinling, Chan Ho Park, Feng He, et al.. (2015). The RhoGAP SPIN6 Associates with SPL11 and OsRac1 and Negatively Regulates Programmed Cell Death and Innate Immunity in Rice. PLoS Pathogens. 11(2). e1004629–e1004629. 102 indexed citations
9.
Césari, Stella, Hiroyuki Kanzaki, Tadashi Fujiwara, et al.. (2014). The NBLRR proteins RGA 4 and RGA 5 interact functionally and physically to confer disease resistance. The EMBO Journal. 33(17). 1941–1959. 274 indexed citations
10.
Sakane, Hiroshi, et al.. (2014). α-Taxilin Interacts with Sorting Nexin 4 and Participates in the Recycling Pathway of Transferrin Receptor. PLoS ONE. 9(4). e93509–e93509. 14 indexed citations
11.
Kawano, Yoji, et al.. (2014). Palmitoylation-dependent Membrane Localization of the Rice Resistance Protein Pit Is Critical for the Activation of the Small GTPase OsRac1. Journal of Biological Chemistry. 289(27). 19079–19088. 31 indexed citations
12.
Akamatsu, Akira, Hann Ling Wong, Masayuki Fujiwara, et al.. (2013). An OsCEBiP/OsCERK1-OsRacGEF1-OsRac1 Module Is an Essential Early Component of Chitin-Induced Rice Immunity. Cell Host & Microbe. 13(4). 465–476. 190 indexed citations
13.
Kim, Sung-Hyun, Tetsuo Oikawa, Junko Kyozuka, et al.. (2012). The bHLH Rac Immunity1 (RAI1) Is Activated by OsRac1 via OsMAPK3 and OsMAPK6 in Rice Immunity. Plant and Cell Physiology. 53(4). 740–754. 74 indexed citations
14.
Kaneko‐Kawano, Takako, Fugo Takasu, Honda Naoki, et al.. (2012). Dynamic Regulation of Myosin Light Chain Phosphorylation by Rho-kinase. PLoS ONE. 7(6). e39269–e39269. 45 indexed citations
16.
Yoshimura, Takeshi, Yoji Kawano, Nariko Arimura, Saeko Kawabata, & Kozo Kaibuchi. (2005). [Molecular mechanisms of neuronal polarity].. PubMed. 25(4). 169–74. 2 indexed citations
17.
Yoshimura, Takeshi, Nariko Arimura, Yoji Kawano, et al.. (2005). Ras regulates neuronal polarity via the PI3-kinase/Akt/GSK-3β/CRMP-2 pathway. Biochemical and Biophysical Research Communications. 340(1). 62–68. 135 indexed citations
18.
Kaneko, Takako, Akio Maeda, Mikito Takefuji, et al.. (2005). Rho mediates endocytosis of epidermal growth factor receptor through phosphorylation of endophilin A1 by Rho‐kinase. Genes to Cells. 10(10). 973–987. 52 indexed citations
19.
Yoshimura, Takeshi, Yoji Kawano, Nariko Arimura, et al.. (2005). GSK-3β Regulates Phosphorylation of CRMP-2 and Neuronal Polarity. Cell. 120(1). 137–149. 751 indexed citations breakdown →
20.
Kandabashi, Tadashi, Hiroaki Shimokawa, Kenji Miyata, et al.. (2000). Inhibition of Myosin Phosphatase by Upregulated Rho-Kinase Plays a Key Role for Coronary Artery Spasm in a Porcine Model With Interleukin-1β. Circulation. 101(11). 1319–1323. 229 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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