Naoko Kageyama

696 total citations
14 papers, 543 citations indexed

About

Naoko Kageyama is a scholar working on Molecular Biology, Nutrition and Dietetics and Spectroscopy. According to data from OpenAlex, Naoko Kageyama has authored 14 papers receiving a total of 543 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 3 papers in Nutrition and Dietetics and 3 papers in Spectroscopy. Recurrent topics in Naoko Kageyama's work include Metabolomics and Mass Spectrometry Studies (6 papers), Mass Spectrometry Techniques and Applications (3 papers) and Microbial Metabolic Engineering and Bioproduction (2 papers). Naoko Kageyama is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (6 papers), Mass Spectrometry Techniques and Applications (3 papers) and Microbial Metabolic Engineering and Bioproduction (2 papers). Naoko Kageyama collaborates with scholars based in Japan and United States. Naoko Kageyama's co-authors include Hiroshi Miyano, Kazutaka Shimbo, Kazuo Hirayama, Junko Yamazaki, Yoshihiro Usuda, Kazuhiko Matsui, Hiroo Yoshida, Akira Imaizumi, Toshimi Mizukoshi and H. Miki and has published in prestigious journals such as American Journal of Respiratory and Critical Care Medicine, Journal of Agricultural and Food Chemistry and Scientific Reports.

In The Last Decade

Naoko Kageyama

14 papers receiving 535 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Naoko Kageyama Japan 11 334 139 75 55 52 14 543
Alexander Bollenbach Germany 14 204 0.6× 144 1.0× 56 0.7× 39 0.7× 23 0.4× 41 550
Maria J. Torres United States 13 336 1.0× 249 1.8× 14 0.2× 28 0.5× 69 1.3× 17 649
Hiroo Yoshida Japan 10 245 0.7× 61 0.4× 134 1.8× 12 0.2× 26 0.5× 19 426
John R. Jefferson United States 14 484 1.4× 118 0.8× 17 0.2× 19 0.3× 64 1.2× 25 738
Rhonda Oetting Deems United States 14 247 0.7× 158 1.1× 20 0.3× 94 1.7× 76 1.5× 24 603
Oded Shaham Israel 6 312 0.9× 191 1.4× 35 0.5× 10 0.2× 84 1.6× 10 517
Dipak K. Das United States 9 131 0.4× 81 0.6× 18 0.2× 16 0.3× 39 0.8× 10 404
Karim Louchami Belgium 14 175 0.5× 156 1.1× 11 0.1× 26 0.5× 50 1.0× 55 533
Katherine Baldwin United States 9 277 0.8× 67 0.5× 41 0.5× 14 0.3× 74 1.4× 12 504
G. Sachse United Kingdom 10 408 1.2× 168 1.2× 10 0.1× 18 0.3× 63 1.2× 19 792

Countries citing papers authored by Naoko Kageyama

Since Specialization
Citations

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

Fields of papers citing papers by Naoko Kageyama

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Naoko Kageyama

This figure shows the co-authorship network connecting the top 25 collaborators of Naoko Kageyama. A scholar is included among the top collaborators of Naoko Kageyama 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 Naoko Kageyama. Naoko Kageyama is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
1.
Kageyama, Naoko, et al.. (2020). Comprehensive Dipeptide Analysis by Pre-column Derivatization LC/MS/MS. BUNSEKI KAGAKU. 69(4.5). 173–178. 1 indexed citations
2.
Imaizumi, Akira, Yusuke Adachi, Takahisa Kawaguchi, et al.. (2019). Genetic basis for plasma amino acid concentrations based on absolute quantification: a genome-wide association study in the Japanese population. European Journal of Human Genetics. 27(4). 621–630. 14 indexed citations
3.
Yamaguchi, Natsu, MH Mahbub, Hidekazu Takahashi, et al.. (2017). Plasma free amino acid profiles evaluate risk of metabolic syndrome, diabetes, dyslipidemia, and hypertension in a large Asian population. Environmental Health and Preventive Medicine. 22(1). 35–35. 61 indexed citations
4.
Mahbub, MH, Natsu Yamaguchi, Hidekazu Takahashi, et al.. (2017). Alteration in plasma free amino acid levels and its association with gout. Environmental Health and Preventive Medicine. 22(1). 7–7. 30 indexed citations
5.
Mahbub, MH, Natsu Yamaguchi, Hidekazu Takahashi, et al.. (2017). Association of plasma free amino acids with hyperuricemia in relation to diabetes mellitus, dyslipidemia, hypertension and metabolic syndrome. Scientific Reports. 7(1). 17616–17616. 17 indexed citations
6.
Nakamura, Hidehiro, Naoko Kageyama, Hiroo Yoshida, et al.. (2016). The Influence of Chyle and Bilirubin on Human Plasma Amino Acid Analysis by High-Performance Liquid Chromatography Ionization Mass Spectrometry. Chromatography. 37(2). 93–97. 2 indexed citations
7.
Tochikubo, Osamu, Hidehiro Nakamura, Hiroko Jinzu, et al.. (2016). Weight loss is associated with plasma free amino acid alterations in subjects with metabolic syndrome. Nutrition and Diabetes. 6(2). e197–e197. 28 indexed citations
8.
Yoshida, Hiroo, Kazuhiro Kondo, Hiroyuki Yamamoto, et al.. (2015). Validation of an analytical method for human plasma free amino acids by high-performance liquid chromatography ionization mass spectrometry using automated precolumn derivatization. Journal of Chromatography B. 998-999. 88–96. 50 indexed citations
9.
Kuroda, Motonaka, Yumiko Kato, Junko Yamazaki, et al.. (2012). Determination of γ-glutamyl-valyl-glycine in raw scallop and processed scallop products using high pressure liquid chromatography–tandem mass spectrometry. Food Chemistry. 134(3). 1640–1644. 34 indexed citations
10.
Usuda, Yoshihiro, Stephen J. Van Dien, Akira Imaizumi, et al.. (2010). Dynamic modeling of Escherichia coli metabolic and regulatory systems for amino-acid production. Journal of Biotechnology. 147(1). 17–30. 51 indexed citations
11.
Shimbo, Kazutaka, Hiroo Yoshida, Naoko Kageyama, et al.. (2009). Automated precolumn derivatization system for analyzing physiological amino acids by liquid chromatography/mass spectrometry. Biomedical Chromatography. 24(7). 683–691. 100 indexed citations
12.
Dien, Stephen Van, Kazutaka Shimbo, Kazuyuki Kubota, et al.. (2006). Determination of metabolic flux changes during fed-batch cultivation from measurements of intracellular amino acids by LC-MS/MS. Journal of Biotechnology. 128(1). 93–111. 64 indexed citations
13.
Yamada, Naoyuki, et al.. (2004). Detection and Quantification of Protein Residues in Food Grade Amino Acids and Nucleic Acids Using a Dot-Blot Fluorescent Staining Method. Journal of Agricultural and Food Chemistry. 52(17). 5329–5333. 8 indexed citations
14.
Miura, M., Haruo Yamauchi, Naoko Kageyama, et al.. (1996). A Neurokinin 1-Receptor Antagonist Improves Exercise-Induced Airway Narrowing in Asthmatic Patients. American Journal of Respiratory and Critical Care Medicine. 153(3). 936–941. 83 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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