Takuya Wada

413 total citations
26 papers, 270 citations indexed

About

Takuya Wada is a scholar working on Plant Science, Genetics and Nutrition and Dietetics. According to data from OpenAlex, Takuya Wada has authored 26 papers receiving a total of 270 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Plant Science, 8 papers in Genetics and 4 papers in Nutrition and Dietetics. Recurrent topics in Takuya Wada's work include GABA and Rice Research (9 papers), Genetic Mapping and Diversity in Plants and Animals (8 papers) and Berry genetics and cultivation research (7 papers). Takuya Wada is often cited by papers focused on GABA and Rice Research (9 papers), Genetic Mapping and Diversity in Plants and Animals (8 papers) and Berry genetics and cultivation research (7 papers). Takuya Wada collaborates with scholars based in Japan, Egypt and Hungary. Takuya Wada's co-authors include Yuji Matsue, Motohiko Kondo, Sachiko Isobe, Takayuki Umemoto, Katsumi Shimomura, Katsunori Miyahara, Norio Iwasawa, Masayuki Miyazaki, Tadashi Tsukaguchi and Osamu Yamaguchi and has published in prestigious journals such as Plant Molecular Biology, Bioscience Biotechnology and Biochemistry and Euphytica.

In The Last Decade

Takuya Wada

25 papers receiving 261 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Takuya Wada Japan 10 237 118 49 33 17 26 270
Yong‐Pei Wu Taiwan 8 246 1.0× 115 1.0× 62 1.3× 36 1.1× 16 0.9× 21 285
Zefu Li China 12 354 1.5× 174 1.5× 65 1.3× 53 1.6× 22 1.3× 28 396
Kajal Kumari India 6 197 0.8× 111 0.9× 21 0.4× 68 2.1× 35 2.1× 8 253
A. K. P. Sivaranjani India 7 261 1.1× 99 0.8× 49 1.0× 55 1.7× 10 0.6× 8 281
Chaoqing Ding China 4 211 0.9× 96 0.8× 19 0.4× 52 1.6× 5 0.3× 7 232
Tingsong Liu China 10 327 1.4× 87 0.7× 40 0.8× 168 5.1× 11 0.6× 12 402
Erchao Duan China 11 353 1.5× 72 0.6× 37 0.8× 161 4.9× 17 1.0× 20 403
Tengqiong Yu China 11 265 1.1× 154 1.3× 20 0.4× 59 1.8× 16 0.9× 23 302
H. P. Moon South Korea 8 287 1.2× 139 1.2× 15 0.3× 83 2.5× 12 0.7× 19 313
Donald E. Obert United States 7 277 1.2× 147 1.2× 43 0.9× 42 1.3× 12 0.7× 8 296

Countries citing papers authored by Takuya Wada

Since Specialization
Citations

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

Fields of papers citing papers by Takuya Wada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Takuya Wada

This figure shows the co-authorship network connecting the top 25 collaborators of Takuya Wada. A scholar is included among the top collaborators of Takuya Wada 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 Takuya Wada. Takuya Wada 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.
Wada, Takuya, Ryo Matsushima, Naoko Fujita, et al.. (2022). Mutation in BEIIb mitigates the negative effect of the mutation in ISA1 on grain filling and amyloplast formation in rice. Plant Molecular Biology. 108(4-5). 497–512. 8 indexed citations
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Shirasawa, Kenta, et al.. (2020). Genome sequence of Hydrangea macrophylla and its application in analysis of the double flower phenotype. DNA Research. 28(1). 16 indexed citations
4.
Wada, Takuya, H. Fuke, Yuki Shimizu, & T. Yoshida. (2020). Application of Machine Learning to the Particle Identification of GAPS. TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES AEROSPACE TECHNOLOGY JAPAN. 18(3). 44–50. 1 indexed citations
5.
Wada, Takuya, et al.. (2020). Detection of Chromosomal Regions for Male Sterility in the Cultivated Strawberry <i>Fragaria</i> × <i>ananassa</i> Duch.. The Horticulture Journal. 89(2). 147–160. 4 indexed citations
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Shimomura, Katsumi, Takuya Wada, Seiya Tanaka, et al.. (2020). DNA markers based on retrotransposon insertion polymorphisms can detect short DNA fragments for strawberry cultivar identification. Breeding Science. 70(2). 231–240. 5 indexed citations
8.
Wada, Takuya, Osamu Yamaguchi, Masayuki Miyazaki, et al.. (2018). Development and characterization of a new rice cultivar, ‘Chikushi-kona 85’, derived from a starch-branching enzyme IIb-deficient mutant line. Breeding Science. 68(2). 278–283. 14 indexed citations
9.
Wada, Takuya, et al.. (2017). Development of a Core Collection of Strawberry Cultivars Based on SSR and CAPS Marker Polymorphisms. The Horticulture Journal. 86(3). 365–378. 9 indexed citations
10.
Kobayashi, Asako, Kazuhiko Sugimoto, Takeshi Hayashi, et al.. (2016). Development of a near isogenic line of ‘Koshihikari’ with a seed dormancy gene and an evaluation of its resistance to heat-induced quality decline. Breeding Research. 18(1). 1–10. 4 indexed citations
11.
Wada, Takuya, Katsunori Miyahara, Tadashi Tsukaguchi, et al.. (2015). Detection of QTLs for white-back and basal-white grains caused by high temperature during ripening period in <i>japonica</i> rice. Breeding Science. 65(3). 216–225. 34 indexed citations
12.
Wada, Takuya, Hideshi Yasui, Takashi Inoue, et al.. (2013). Validation of QTLs for Eating Quality of Japonica Rice ‘Koshihikari’ Using Backcross Inbred Lines. Plant Production Science. 16(2). 131–140. 4 indexed citations
13.
Kitaoka, Momoko, Takuya Wada, Takeshi Nishio, & Masahiro Goto. (2010). Fluorogenic Ribonuclease Protection (FRIP) Analysis of Single Nucleotide Polymorphisms (SNPs) in Japanese Rice (Oryza sativaL.) DNA for Cultivar Discrimination. Bioscience Biotechnology and Biochemistry. 74(11). 2189–2193. 2 indexed citations
14.
Wada, Takuya, et al.. (2010). Starch Eluted from Polished Rice during Soaking in Hot Water is Related to the Eating Quality of Cooked Rice. Journal of Applied Glycoscience. 58(1). 13–18. 12 indexed citations
15.
Wada, Takuya, et al.. (2008). Effective selection method of rice varieties with resistance to high-temperature during riping period by warm water treatment. 21–23. 2 indexed citations
16.
Wada, Takuya, et al.. (2006). Mapping of QTLs for Physicochemical Properties in Japonica Rice. Breeding Science. 56(3). 253–260. 32 indexed citations
17.
Yoshida, T., Takuya Wada, Hiroshi Motoda, & Takashi Washio. (2004). Adaptive Ripple Down Rules method based on minimum description length principle. Intelligent Data Analysis. 8(3). 239–265. 3 indexed citations
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Wada, Takuya, Hiroshi Motoda, & Takashi Washio. (2001). Integrating Inductive Learning to the Ripple Down Rules Method with the Minimum Description Length Principle. Transactions of the Japanese Society for Artificial Intelligence. 16. 268–278.
20.
Wada, Takuya, Tadashi Horiuchi, Hiroshi Motoda, & Takashi Washio. (2001). A Description Length-Based Decision Criterion for Default Knowledge in the Ripple Down Rules Method. Knowledge and Information Systems. 3(2). 146–167. 3 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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