M. Kohno

3.1k total citations
114 papers, 2.4k citations indexed

About

M. Kohno is a scholar working on Nuclear and High Energy Physics, Atomic and Molecular Physics, and Optics and Molecular Biology. According to data from OpenAlex, M. Kohno has authored 114 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 62 papers in Nuclear and High Energy Physics, 21 papers in Atomic and Molecular Physics, and Optics and 17 papers in Molecular Biology. Recurrent topics in M. Kohno's work include Nuclear physics research studies (46 papers), Quantum Chromodynamics and Particle Interactions (44 papers) and High-Energy Particle Collisions Research (24 papers). M. Kohno is often cited by papers focused on Nuclear physics research studies (46 papers), Quantum Chromodynamics and Particle Interactions (44 papers) and High-Energy Particle Collisions Research (24 papers). M. Kohno collaborates with scholars based in Japan, Germany and United States. M. Kohno's co-authors include W. Weise, Motohiko Hirotsuka, Y. Fujiwara, Makoto Kito, Wataru Kugimiya, Nobuhiko Tachibana, Y. Suzuki, K. Miyagawa, Motohiro Maebuchi and Yukio Hashimoto and has published in prestigious journals such as Journal of Molecular Biology, Journal of Agricultural and Food Chemistry and Food Chemistry.

In The Last Decade

M. Kohno

111 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M. Kohno Japan 27 878 618 403 291 283 114 2.4k
Yukio Hashimoto Japan 19 360 0.4× 548 0.9× 172 0.4× 256 0.9× 344 1.2× 99 1.6k
Yoshihisa Yano Japan 32 219 0.2× 1.4k 2.3× 111 0.3× 115 0.4× 148 0.5× 124 2.7k
D. Müller Germany 38 3.1k 3.5× 710 1.1× 115 0.3× 39 0.1× 36 0.1× 171 5.1k
Kee‐Tae Kim South Korea 29 73 0.1× 1.2k 1.9× 679 1.7× 43 0.1× 263 0.9× 137 2.7k
Thomas F. Walsh United States 28 1.3k 1.4× 212 0.3× 64 0.2× 78 0.3× 35 0.1× 111 2.4k
Klaus Zangger Austria 37 816 0.9× 1.7k 2.8× 52 0.1× 168 0.6× 23 0.1× 142 4.2k
P. Koehler United States 30 771 0.9× 601 1.0× 490 1.2× 195 0.7× 16 0.1× 187 2.7k
Hye‐Sung Lee United States 27 1.9k 2.1× 195 0.3× 111 0.3× 152 0.5× 59 0.2× 83 2.5k
Yoav Peleg Israel 32 194 0.2× 2.2k 3.5× 118 0.3× 161 0.6× 13 0.0× 105 3.5k
J. Schreiber Germany 19 805 0.9× 2.3k 3.7× 21 0.1× 550 1.9× 31 0.1× 45 3.9k

Countries citing papers authored by M. Kohno

Since Specialization
Citations

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

Fields of papers citing papers by M. Kohno

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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

This figure shows the co-authorship network connecting the top 25 collaborators of M. Kohno. A scholar is included among the top collaborators of M. Kohno 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. Kohno. M. Kohno 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.
Li, Xuan, Junki Miyamoto, Miki Igarashi, et al.. (2018). Dietary mung bean protein reduces high-fat diet-induced weight gain by modulating host bile acid metabolism in a gut microbiota-dependent manner. Biochemical and Biophysical Research Communications. 501(4). 955–961. 75 indexed citations
2.
3.
Kohno, M.. (2017). Soybean Protein and Peptide as Complementation Medical Food Materials for Treatment of Dyslipidemia and Inflammatory Disorders. Food Science and Technology Research. 23(6). 773–782. 6 indexed citations
4.
Kohno, M., et al.. (2017). Improvement of glucose metabolism via mung bean protein consumption: A clinical trial of GLUCODIATM isolated mung bean protein in Japan. Functional Foods in Health and Disease. 7(2). 115–115. 8 indexed citations
5.
Watanabe, Hitoshi, Y. Inaba, Kumi Kimura, et al.. (2016). Dietary Mung Bean Protein Reduces Hepatic Steatosis, Fibrosis, and Inflammation in Male Mice with Diet-Induced, Nonalcoholic Fatty Liver Disease. Journal of Nutrition. 147(1). 52–60. 40 indexed citations
6.
Yamashita, Yoko, Manabu Wakagi, Nobuhiko Tachibana, et al.. (2015). β-Conglycinin Peptides Improve Glucose Uptake through the AMPK Signaling Pathway in L6 Myotubes. Food Science and Technology Research. 21(5). 727–732. 4 indexed citations
7.
Wanezaki, Satoshi, Nobuhiko Tachibana, Shintaro Saito, et al.. (2014). Soy β-conglycinin improves obesity-induced metabolic abnormalities in a rat model of nonalcoholic fatty liver disease. Obesity Research & Clinical Practice. 9(2). 168–174. 35 indexed citations
8.
Inoue, Yoshiki, Sachie K. Ogawa, Takumi Yamane, et al.. (2012). Accelerating Effect of Soy Peptides Containing Collagen Peptides on Type I and III Collagen Levels in Rat Skin. Bioscience Biotechnology and Biochemistry. 76(8). 1549–1551. 6 indexed citations
9.
Tachibana, Nobuhiko, et al.. (2010). β-Conglycinin Lowers Very-Low-Density Lipoprotein-Triglyceride Levels by Increasing Adiponectin and Insulin Sensitivity in Rats. Bioscience Biotechnology and Biochemistry. 74(6). 1250–1255. 37 indexed citations
10.
Kojima, Makiko, Nobuhiko Tachibana, Satoshi Seino, et al.. (2010). Structured triacylglycerol containing behenic and oleic acids suppresses triacylglycerol absorption and prevents obesity in rats. Lipids in Health and Disease. 9(1). 77–77. 43 indexed citations
11.
Mochizuki, Yuko, Motohiro Maebuchi, M. Kohno, et al.. (2009). Changes in Lipid Metabolism by Soy β-Conglycinin-Derived Peptides in HepG2 Cells. Journal of Agricultural and Food Chemistry. 57(4). 1473–1480. 46 indexed citations
12.
Maebuchi, Motohiro, Masahiko Samoto, M. Kohno, et al.. (2007). Improvement in the Intestinal Absorption of Soy Protein By Enzymatic Digestion to Oligopeptide in Healthy Adult Men. Food Science and Technology Research. 13(1). 45–53. 56 indexed citations
13.
Masuda, Kenichi, Motohiro Maebuchi, Masahiko Samoto, et al.. (2007). Effect of soy-peptide intake on exercise-induced muscle damage. 15(2). 228–235. 7 indexed citations
14.
15.
Kohno, M., et al.. (2004). SU 6 クオークモデルのバリオン-バリオン相互作用を用いる 6 ΛΛ HeのFaddeev計算. Physical review. C. 70(3). 1–37001. 3 indexed citations
16.
Yu, Yang, Katsuyuki Ohmori, Ikuko Kondo, et al.. (2002). Correlation of functional and structural alterations of the coronary arterioles during development of type II diabetes mellitus in rats. Cardiovascular Research. 56(2). 303–311. 36 indexed citations
17.
AOYAMA, T., M. Kohno, Tsutomu Saito, et al.. (2001). Reduction by Phytate-reduced Soybean β-Conglycinin of Plasma Triglyceride Level of Young and Adult Rats. Bioscience Biotechnology and Biochemistry. 65(5). 1071–1075. 66 indexed citations
18.
Saito, Tsutomu, M. Kohno, Kazunobu Tsumura, Wataru Kugimiya, & Makoto Kito. (2001). Novel Method Using Phytase for Separating Soybean β-Conglycinin and Glycinin. Bioscience Biotechnology and Biochemistry. 65(4). 884–887. 50 indexed citations
19.
Kohno, M., et al.. (1998). Cloning of Genomic DNA ofRhizopus niveusLipase and Expression in the YeastSaccharomyces cerevisiae. Bioscience Biotechnology and Biochemistry. 62(12). 2425–2427. 6 indexed citations
20.
Kugimiya, Wataru, et al.. (1992). Cloning and Sequence Analysis of cDNA encodingRhizopus niveusLipase. Bioscience Biotechnology and Biochemistry. 56(5). 716–719. 26 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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