Mai Tsuda

737 total citations
36 papers, 498 citations indexed

About

Mai Tsuda is a scholar working on Plant Science, Molecular Biology and Genetics. According to data from OpenAlex, Mai Tsuda has authored 36 papers receiving a total of 498 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Plant Science, 19 papers in Molecular Biology and 8 papers in Genetics. Recurrent topics in Mai Tsuda's work include Plant tissue culture and regeneration (10 papers), CRISPR and Genetic Engineering (9 papers) and Genetically Modified Organisms Research (7 papers). Mai Tsuda is often cited by papers focused on Plant tissue culture and regeneration (10 papers), CRISPR and Genetic Engineering (9 papers) and Genetically Modified Organisms Research (7 papers). Mai Tsuda collaborates with scholars based in Japan, United States and Spain. Mai Tsuda's co-authors include Yutaka Tabei, Ken-ichi Konagaya, Ayako Okuzaki, Ryo Ohsawa, Yoshihiko Nanasato, Masaaki Yoshikawa, Yuichi Takeuchi, Kazuo Watanabe, Sugihiro Ando and Akito Kaga and has published in prestigious journals such as PLoS ONE, Scientific Reports and Frontiers in Plant Science.

In The Last Decade

Mai Tsuda

36 papers receiving 478 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mai Tsuda Japan 12 385 326 88 78 22 36 498
Jana Ordon Germany 12 541 1.4× 353 1.1× 63 0.7× 37 0.5× 39 1.8× 15 687
Nancy Podevin Belgium 9 342 0.9× 416 1.3× 91 1.0× 56 0.7× 15 0.7× 13 512
Jamie McCuiston United States 7 609 1.6× 507 1.6× 41 0.5× 100 1.3× 12 0.5× 7 726
N. Christov Bulgaria 14 528 1.4× 237 0.7× 43 0.5× 107 1.4× 13 0.6× 44 615
Florence Charlot France 16 600 1.6× 556 1.7× 61 0.7× 25 0.3× 29 1.3× 31 762
Jean‐Michel Michno United States 13 570 1.5× 440 1.3× 42 0.5× 123 1.6× 7 0.3× 22 737
Yuming Lu China 13 708 1.8× 763 2.3× 57 0.6× 83 1.1× 30 1.4× 21 971
Karen Massel Australia 9 293 0.8× 271 0.8× 32 0.4× 74 0.9× 18 0.8× 15 439
Stijn Aesaert Belgium 14 426 1.1× 440 1.3× 62 0.7× 62 0.8× 10 0.5× 21 586
Burcu Alptekin United States 10 509 1.3× 303 0.9× 22 0.3× 45 0.6× 19 0.9× 17 637

Countries citing papers authored by Mai Tsuda

Since Specialization
Citations

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

Fields of papers citing papers by Mai Tsuda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mai Tsuda

This figure shows the co-authorship network connecting the top 25 collaborators of Mai Tsuda. A scholar is included among the top collaborators of Mai Tsuda 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 Mai Tsuda. Mai Tsuda 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.
Barras, C., Yoshihiro Ohmori, Yuji Yamasaki, et al.. (2024). High-Throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models. Plant Phenomics. 6. 244–244. 7 indexed citations
2.
Ohmori, Yoshihiro, Yuji Yamasaki, Hirokazu Takahashi, et al.. (2024). Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean. Theoretical and Applied Genetics. 137(4). 77–77. 4 indexed citations
3.
Ohmori, Yoshihiro, Yuji Yamasaki, Hirokazu Takahashi, et al.. (2023). Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data. Frontiers in Plant Science. 14. 1201806–1201806. 5 indexed citations
4.
Kajiya‐Kanegae, Hiromi, Yoshihiro Ohmori, Yuji Yamasaki, et al.. (2022). Time‐series multispectral imaging in soybean for improving biomass and genomic prediction accuracy. The Plant Genome. 15(4). e20244–e20244. 10 indexed citations
5.
Yoshida, Hideki, Yoshihiro Omori, Mai Tsuda, et al.. (2022). Effects of irrigation on root growth and development of soybean: A 3-year sandy field experiment. Frontiers in Plant Science. 13. 1047563–1047563. 5 indexed citations
6.
Ohmori, Yoshihiro, Yuji Yamasaki, Hirokazu Takahashi, et al.. (2022). Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations. Frontiers in Plant Science. 13. 828864–828864. 8 indexed citations
7.
Sekine, Daisuke, Mai Tsuda, Shiori Yabe, et al.. (2021). Improving Quantitative Traits in Self-Pollinated Crops Using Simulation-Based Selection With Minimal Crossing. Frontiers in Plant Science. 12. 729645–729645. 3 indexed citations
8.
Itoh, Takeshi, Mai Tsuda, M. Endo, et al.. (2020). Foreign DNA detection by high-throughput sequencing to regulate genome-edited agricultural products. Scientific Reports. 10(1). 4914–4914. 18 indexed citations
9.
Watanabe, Daiki, et al.. (2020). Increased awareness and decreased acceptance of genome-editing technology: The impact of the Chinese twin babies. PLoS ONE. 15(9). e0238128–e0238128. 16 indexed citations
10.
11.
Tsuda, Mai, Kazuo Watanabe, & Ryo Ohsawa. (2019). Regulatory Status of Genome-Edited Organisms Under the Japanese Cartagena Act. Frontiers in Bioengineering and Biotechnology. 7. 387–387. 46 indexed citations
12.
Tsuda, Mai, Akito Kaga, Toyoaki Anai, et al.. (2015). Construction of a high-density mutant library in soybean and development of a mutant retrieval method using amplicon sequencing. BMC Genomics. 16(1). 1014–1014. 75 indexed citations
13.
Tsuda, Mai, et al.. (2014). Development of methods for risk assessment of transgenic silkworms rearing on biodiversity. 83(2). 171–179. 1 indexed citations
14.
Tsuda, Mai, Morihiko Tamai, & Keiichi Yasumoto. (2014). A Monitoring Support System for Elderly Person Living Alone through Activity Sensing in Living Space and Its Evaluation. 2014. 2 indexed citations
15.
Konagaya, Ken-ichi, Mai Tsuda, Ayako Okuzaki, Sugihiro Ando, & Yutaka Tabei. (2013). Application of the acetolactate synthase gene as a cisgenic selectable marker for Agrobacterium-mediated transformation in Chinese cabbage (Brassica rapa ssp. pekinensis). Plant Biotechnology. 30(2). 125–133. 9 indexed citations
16.
Nanasato, Yoshihiko, Ken-ichi Konagaya, Ayako Okuzaki, Mai Tsuda, & Yutaka Tabei. (2012). Improvement of Agrobacterium-mediated transformation of cucumber (Cucumis sativus L.) by combination of vacuum infiltration and co-cultivation on filter paper wicks. Plant Biotechnology Reports. 7(3). 267–276. 47 indexed citations
17.
Nanasato, Yoshihiko, Ken-ichi Konagaya, Ayako Okuzaki, Mai Tsuda, & Yutaka Tabei. (2011). Agrobacterium-mediated transformation of kabocha squash (Cucurbita moschata Duch) induced by wounding with aluminum borate whiskers. Plant Cell Reports. 30(8). 1455–1464. 27 indexed citations
18.
19.
Makihara, Daigo, et al.. (2000). Changes of rice sodium content due to sodium exclusion and transpiration under salinity.. Okayama University Scientific Achievement Repository (Okayama University). 31–37. 2 indexed citations
20.
Tsuda, Mai, et al.. (1985). Sexuality for the teleomorph formation and conidial variability in Curvularia lunata.. 26(1). 27–39. 4 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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