M. Takayama

2.5k total citations · 1 hit paper
40 papers, 1.8k citations indexed

About

M. Takayama is a scholar working on Plant Science, Molecular Biology and Electrical and Electronic Engineering. According to data from OpenAlex, M. Takayama has authored 40 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Plant Science, 14 papers in Molecular Biology and 10 papers in Electrical and Electronic Engineering. Recurrent topics in M. Takayama's work include GABA and Rice Research (11 papers), Plant Genetic and Mutation Studies (8 papers) and Medicinal Plants and Neuroprotection (5 papers). M. Takayama is often cited by papers focused on GABA and Rice Research (11 papers), Plant Genetic and Mutation Studies (8 papers) and Medicinal Plants and Neuroprotection (5 papers). M. Takayama collaborates with scholars based in Japan, United States and Hungary. M. Takayama's co-authors include Hiroshi Ezura, Chiaki Matsukura, Tohru Ariizumi, Satoko Nonaka, Chikako Arai, Sachiko Kashojiya, Kenji Miura, Akihiko Kondo, Keiji Nishida and Zenpei Shimatani and has published in prestigious journals such as Blood, Nature Biotechnology and Molecular and Cellular Biology.

In The Last Decade

M. Takayama

39 papers receiving 1.8k citations

Hit Papers

Targeted base editing in rice and tomato using a CRISPR-C... 2017 2026 2020 2023 2017 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Targeted base editing in rice and tomato using a CRISPR-Cas9 cytidine deaminase fusion
    2017 544 citations Zenpei Shimatani, Sachiko Kashojiya et al. Nature Biotechnology profile →

Peers

M. Takayama
Jianhua Chen China
Michihiko Fujii Japan
Hiroaki Taniguchi Japan
András Szilágyi Hungary
Herbert Lindner Austria
Darryl B. Hardie Canada
Wei-Chi Wang China
Dário Eluan Kalume Brazil
I. V. Smirnov Russia
Qiuling Huang China
Jianhua Chen China
M. Takayama
Citations per year, relative to M. Takayama M. Takayama (= 1×) peers Jianhua Chen

Countries citing papers authored by M. Takayama

Since Specialization
Citations

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

Fields of papers citing papers by M. Takayama

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Takayama

This figure shows the co-authorship network connecting the top 25 collaborators of M. Takayama. A scholar is included among the top collaborators of M. Takayama 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 M. Takayama. M. Takayama 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.
Gramazio, Pietro, M. Takayama, & Hiroshi Ezura. (2020). Challenges and Prospects of New Plant Breeding Techniques for GABA Improvement in Crops: Tomato as an Example. Frontiers in Plant Science. 11. 577980–577980. 45 indexed citations
2.
Yamaguchi, M., et al.. (2019). 155 Fertility of sexed semen in Holstein heifers and cows. Reproduction Fertility and Development. 32(2). 204–204. 1 indexed citations
3.
Lee, Jeongeun, Satoko Nonaka, M. Takayama, & Hiroshi Ezura. (2018). Utilization of a Genome-Edited Tomato (Solanum lycopersicum) with High Gamma Aminobutyric Acid Content in Hybrid Breeding. Journal of Agricultural and Food Chemistry. 66(4). 963–971. 24 indexed citations
4.
Nonaka, Satoko, Chikako Arai, M. Takayama, Chiaki Matsukura, & Hiroshi Ezura. (2017). Efficient increase of ɣ-aminobutyric acid (GABA) content in tomato fruits by targeted mutagenesis. Scientific Reports. 7(1). 7057–7057. 216 indexed citations
5.
Shimatani, Zenpei, Sachiko Kashojiya, M. Takayama, et al.. (2017). Targeted base editing in rice and tomato using a CRISPR-Cas9 cytidine deaminase fusion. Nature Biotechnology. 35(5). 441–443. 544 indexed citations breakdown →
6.
Nonaka, Satoko, Tatsuhiko Someya, Zhou Sha, et al.. (2017). An Agrobacterium tumefaciens Strain with Gamma-Aminobutyric Acid Transaminase Activity Shows an Enhanced Genetic Transformation Ability in Plants. Scientific Reports. 7(1). 42649–42649. 28 indexed citations
7.
Takayama, M., et al.. (2016). Design of high torque PM vernier motor. International Conference on Electrical Machines and Systems. 9 indexed citations
8.
Takayama, M., Chiaki Matsukura, Tohru Ariizumi, & Hiroshi Ezura. (2016). Activating glutamate decarboxylase activity by removing the autoinhibitory domain leads to hyper γ-aminobutyric acid (GABA) accumulation in tomato fruit. Plant Cell Reports. 36(1). 103–116. 38 indexed citations
9.
Takayama, M., Satoshi Koike, Miyako Kusano, et al.. (2015). Tomato Glutamate Decarboxylase GenesSlGAD2andSlGAD3Play Key Roles in Regulating γ-Aminobutyric Acid Levels in Tomato (Solanum lycopersicum). Plant and Cell Physiology. 56(8). 1533–1545. 48 indexed citations
10.
Takayama, M. & Hiroshi Ezura. (2015). How and why does tomato accumulate a large amount of GABA in the fruit?. Frontiers in Plant Science. 6. 612–612. 94 indexed citations
11.
12.
Takayama, M., et al.. (2013). Investigation of Magnetic Pole Combination in a Surface Permanent Magnet-type Vernier Motor. Journal of the Magnetics Society of Japan. 37(3-2). 268–272. 2 indexed citations
13.
Takayama, M., et al.. (2010). The Exudate of Pressure Ulcers Contains a Substantial Amount of Vascular Endothelial Growth Factor. The Tohoku Journal of Experimental Medicine. 221(4). 315–319. 3 indexed citations
14.
Motohashi, Hozumi, Momoko Kimura, Rie Fujita, et al.. (2009). NF-E2 domination over Nrf2 promotes ROS accumulation and megakaryocytic maturation. Blood. 115(3). 677–686. 86 indexed citations
15.
16.
Takeda, Motohiro, Masaki Kobayashi, M. Takayama, et al.. (2004). Biophoton detection as a novel technique for cancer imaging. Cancer Science. 95(8). 656–661. 83 indexed citations
17.
Than, Nándor Gábor, Atsuo Itakura, Ch.V. Rao, et al.. (2003). Clinical Applications of Pregnancy-Related Proteins—A Workshop Report. Placenta. 24. S60–S64. 16 indexed citations
18.
Isaka, Keiichi, Hiroe Ito, Yasukazu Sagawa, et al.. (2002). Expression and Activity of Matrix Metalloproteinase 2 and 9 in Human Trophoblasts. Placenta. 24(1). 53–64. 204 indexed citations
19.
Watanabe, Takafumi, Issei Imoto, Yumi Fukuda, et al.. (2001). Human arylhydrocarbon receptor repressor (AHRR) gene: genomic structure and analysis of polymorphism in endometriosis. Journal of Human Genetics. 46(6). 342–346. 80 indexed citations
20.
Sato, Nobuyoshi, M. Takayama, Noriyuki Yoshimoto, & M. Yoshizawa. (1999). Growth and structure of Ce/Cu on GaAs(110) substrate by MBE method. Physica B Condensed Matter. 259-261. 123–125. 1 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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