Tokio Imbe

1.8k total citations
44 papers, 1.3k citations indexed

About

Tokio Imbe is a scholar working on Plant Science, Genetics and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Tokio Imbe has authored 44 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Plant Science, 20 papers in Genetics and 5 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Tokio Imbe's work include Rice Cultivation and Yield Improvement (20 papers), Genetic Mapping and Diversity in Plants and Animals (20 papers) and GABA and Rice Research (16 papers). Tokio Imbe is often cited by papers focused on Rice Cultivation and Yield Improvement (20 papers), Genetic Mapping and Diversity in Plants and Animals (20 papers) and GABA and Rice Research (16 papers). Tokio Imbe collaborates with scholars based in Japan, Philippines and Egypt. Tokio Imbe's co-authors include Hiroshi Kato, Ikuo Ando, Hiroyuki Sato, Hiroshi Tsunematsu, Hiroshi Nemoto, L. A. Ebron, Makoto Sakai, Yoshimichi Fukuta, Nagao Hayashi and Hideyuki Hirabayashi and has published in prestigious journals such as Theoretical and Applied Genetics, Field Crops Research and Crop Science.

In The Last Decade

Tokio Imbe

44 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tokio Imbe Japan 22 1.2k 582 227 175 112 44 1.3k
Ikuo Ando Japan 17 1.1k 0.9× 609 1.0× 209 0.9× 114 0.7× 90 0.8× 37 1.2k
W. D. Park United States 13 1.5k 1.2× 732 1.3× 417 1.8× 76 0.4× 156 1.4× 14 1.7k
Chiara Biselli Italy 17 871 0.7× 228 0.4× 245 1.1× 173 1.0× 82 0.7× 25 985
Hideyuki Hirabayashi Japan 20 1.1k 0.9× 549 0.9× 131 0.6× 35 0.2× 174 1.6× 39 1.2k
Francesca Desiderio Italy 15 658 0.6× 294 0.5× 144 0.6× 84 0.5× 80 0.7× 31 769
Jung-Pil Suh South Korea 17 1.3k 1.1× 585 1.0× 209 0.9× 87 0.5× 43 0.4× 62 1.3k
L. S. Lee Australia 6 651 0.5× 264 0.5× 222 1.0× 45 0.3× 83 0.7× 8 875
Yosef Burger Israel 21 1.2k 1.0× 289 0.5× 229 1.0× 258 1.5× 24 0.2× 45 1.3k
Hyeonso Ji South Korea 16 796 0.7× 334 0.6× 207 0.9× 65 0.4× 27 0.2× 56 904
Melissa H. Jia United States 20 1.1k 1.0× 604 1.0× 366 1.6× 128 0.7× 21 0.2× 48 1.3k

Countries citing papers authored by Tokio Imbe

Since Specialization
Citations

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

Fields of papers citing papers by Tokio Imbe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tokio Imbe

This figure shows the co-authorship network connecting the top 25 collaborators of Tokio Imbe. A scholar is included among the top collaborators of Tokio Imbe 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 Tokio Imbe. Tokio Imbe 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.
Suzuki, Keitaro, Hideyuki Hirabayashi, Osamu Ideta, et al.. (2015). qAC2, a novel QTL that interacts with Wx and controls the low amylose content in rice (Oryza sativa L.). Theoretical and Applied Genetics. 128(4). 563–573. 28 indexed citations
2.
Mizobuchi, Ritsuko, Hiroyuki Sato, Shuichi Fukuoka, et al.. (2013). Mapping a quantitative trait locus for resistance to bacterial grain rot in rice. Rice. 6(1). 13–13. 24 indexed citations
3.
Mizobuchi, Ritsuko, Hiroyuki Sato, Shuichi Fukuoka, et al.. (2013). Identification of qRBS1, a QTL involved in resistance to bacterial seedling rot in rice. Theoretical and Applied Genetics. 126(9). 2417–2425. 19 indexed citations
4.
Ideta, Osamu, Izumi Kono, Yoshinobu Takeuchi, et al.. (2012). Diversity and relationships between coefficient of parentage and genetic distance estimated by SSR markers in Japanese rice cultivars. Breeding Research. 14(4). 106–113. 2 indexed citations
5.
Koide, Yohei, L. A. Ebron, Hiroshi Kato, et al.. (2011). A set of near-isogenic lines for blast resistance genes with an Indica-type rainfed lowland elite rice (Oryza sativa L.) genetic background. Field Crops Research. 123(1). 19–27. 23 indexed citations
6.
Laza, M., et al.. (2009). Quantitative trait loci for stomatal density and size in lowland rice. Euphytica. 172(2). 149–158. 39 indexed citations
7.
Fujita, Daisuke, L. A. Ebron, Mary Jeanie Telebanco‐Yanoria, et al.. (2009). Development of introgression lines of an Indica-type rice variety, IR64, for unique agronomic traits and detection of the responsible chromosomal regions. Field Crops Research. 114(2). 244–254. 43 indexed citations
8.
Takeuchi, Yoshinobu, Kiyosumi Hori, Keitaro Suzuki, et al.. (2008). Major QTLs for eating quality of an elite Japanese rice cultivar, Koshihikari, on the short arm of chromosome 3. Breeding Science. 58(4). 437–445. 56 indexed citations
9.
Nakai, Hiroyuki, Tatsuya Ito, Young‐Min Kim, et al.. (2008). Rice α-glucosidase isozymes and isoforms showing different starch granules-binding and -degrading ability. Biocatalysis and Biotransformation. 26(1-2). 104–110. 3 indexed citations
10.
Nakai, Hiroyuki, Tatsuya Ito, Kiwamu Kamiya, et al.. (2007). Function-unknown Glycoside Hydrolase Family 31 Proteins, mRNAs of which were Expressed in Rice Ripening and Germinating Stages, are  -Glucosidase and  -Xylosidase. The Journal of Biochemistry. 142(4). 491–500. 17 indexed citations
11.
Laza, M., et al.. (2006). Identification of Quantitative Trait Loci for δ13C and Productivity in Irrigated Lowland Rice. Crop Science. 46(2). 763–773. 40 indexed citations
12.
Ebron, L. A., Yoshimichi Fukuta, Tokio Imbe, et al.. (2005). Identification of blast resistance genes in elite Indica-type varieties of rice (Oryza sativa L.).. SABRAO Journal of Breeding and Genetics. 37(1). 19–31. 4 indexed citations
13.
Sakai, Makoto, et al.. (2003). New Rice Varieties for Whole Crop Silage Use in Japan. Breeding Science. 53(3). 271–275. 49 indexed citations
14.
Sato, Hiroyuki, Yasuhíro Suzuki, Makoto Sakai, & Tokio Imbe. (2002). Molecular Characterization of Wx-mq, a Novel Mutant Gene for Low-amylose Content in Endosperm of Rice (Oryza sativa L.).. Breeding Science. 52(2). 131–135. 96 indexed citations
15.
Nakane, Akihiro, et al.. (2001). "Milky Queen, a new high-quality rice cultivar with low amylose content in endosperm.". 13 indexed citations
16.
17.
Imbe, Tokio, et al.. (1993). Resistance in Some Japonica Rice Cultivars to Rice Tungro Spherical Virus. Ikushugaku zasshi. 43(4). 549–556. 3 indexed citations
18.
Zakri, A. H., et al.. (1990). Inheritance of tolerance to rice tungro bacilliform virus (RTBV) in rice (Oryza sativa L.). Theoretical and Applied Genetics. 80(4). 513–517. 17 indexed citations
19.
Imbe, Tokio, et al.. (1987). Inheritance of resistance to the green rice leafhopper Nephotettix cincticeps UHLER and dwarf disease in Rice Norin-PL 5.. Ikushugaku zasshi. 37(2). 177–184. 7 indexed citations
20.
Imbe, Tokio, et al.. (1986). Varietal resistance of rice to bacterial grain rot and screening method.. Kyushu Plant Protection Research. 32. 17–19. 6 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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