Yasuhiro Date

2.7k total citations
53 papers, 2.0k citations indexed

About

Yasuhiro Date is a scholar working on Molecular Biology, Ecology and Analytical Chemistry. According to data from OpenAlex, Yasuhiro Date has authored 53 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Molecular Biology, 13 papers in Ecology and 8 papers in Analytical Chemistry. Recurrent topics in Yasuhiro Date's work include Metabolomics and Mass Spectrometry Studies (30 papers), Gut microbiota and health (11 papers) and Spectroscopy and Chemometric Analyses (8 papers). Yasuhiro Date is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (30 papers), Gut microbiota and health (11 papers) and Spectroscopy and Chemometric Analyses (8 papers). Yasuhiro Date collaborates with scholars based in Japan, United States and Canada. Yasuhiro Date's co-authors include Jun Kikuchi, Satoshi Tsuneda, Tatsuo Sumino, Kazuichi Isaka, Kenji Sakata, Toshifumi Osaka, Kengo Ito, Hiroshi Ohno, Tamotsu Kato and Sachiko Yoshie and has published in prestigious journals such as Gastroenterology, PLoS ONE and Analytical Chemistry.

In The Last Decade

Yasuhiro Date

52 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yasuhiro Date Japan 27 907 448 223 207 200 53 2.0k
Per Bruheim Norway 33 2.0k 2.2× 259 0.6× 439 2.0× 200 1.0× 50 0.3× 111 3.6k
Sergey Kucheryavskiy Denmark 19 272 0.3× 161 0.4× 382 1.7× 181 0.9× 60 0.3× 53 1.5k
Tetsuya Sasaki Japan 23 296 0.3× 251 0.6× 115 0.5× 122 0.6× 46 0.2× 107 1.4k
Xiaoling Liu China 28 1.2k 1.4× 200 0.4× 318 1.4× 176 0.9× 56 0.3× 135 3.1k
Jae‐Jin Lee South Korea 24 790 0.9× 202 0.5× 85 0.4× 461 2.2× 106 0.5× 117 1.5k
Jeff Cole United Kingdom 35 1.9k 2.1× 742 1.7× 165 0.7× 774 3.7× 504 2.5× 85 4.5k
Hongwei Liu China 29 1.3k 1.4× 434 1.0× 423 1.9× 284 1.4× 68 0.3× 121 3.3k
Jonas S. Almeida Portugal 18 759 0.8× 148 0.3× 479 2.1× 88 0.4× 90 0.5× 24 1.7k
Michael J. Larkin United Kingdom 29 1.1k 1.2× 1.2k 2.7× 241 1.1× 525 2.5× 113 0.6× 80 2.7k
Iwao Ohtsu Japan 34 959 1.1× 45 0.1× 254 1.1× 930 4.5× 186 0.9× 143 2.8k

Countries citing papers authored by Yasuhiro Date

Since Specialization
Citations

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

Fields of papers citing papers by Yasuhiro Date

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yasuhiro Date

This figure shows the co-authorship network connecting the top 25 collaborators of Yasuhiro Date. A scholar is included among the top collaborators of Yasuhiro Date 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 Yasuhiro Date. Yasuhiro Date 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.
Date, Yasuhiro, Makoto Umeda, Yusuke Tarumoto, et al.. (2024). A Data-Driven Approach to Sugarcane Breeding Programs with Agronomic Characteristics and Amino Acid Constituent Profiling. Metabolites. 14(4). 243–243. 1 indexed citations
2.
Date, Yasuhiro, Feifei Wei, Yuuri Tsuboi, et al.. (2021). Relaxometric learning: a pattern recognition method for T2 relaxation curves based on machine learning supported by an analytical framework. BMC Chemistry. 15(1). 13–13. 8 indexed citations
3.
Wei, Feifei, Minoru Fukuchi, Kengo Ito, et al.. (2020). Large-Scale Evaluation of Major Soluble Macromolecular Components of Fish Muscle from a Conventional 1H-NMR Spectral Database. Molecules. 25(8). 1966–1966. 9 indexed citations
4.
Oita, Azusa, Yuuri Tsuboi, Yasuhiro Date, et al.. (2018). Profiling physicochemical and planktonic features from discretely/continuously sampled surface water. The Science of The Total Environment. 636. 12–19. 8 indexed citations
5.
Ito, Kengo, et al.. (2018). Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals. Chemical Science. 9(43). 8213–8220. 20 indexed citations
6.
Wei, Feifei, et al.. (2018). Systemic Homeostasis in Metabolome, Ionome, and Microbiome of Wild Yellowfin Goby in Estuarine Ecosystem. Scientific Reports. 8(1). 3478–3478. 25 indexed citations
7.
8.
Date, Yasuhiro, et al.. (2018). Application of ensemble deep neural network to metabolomics studies. Analytica Chimica Acta. 1037. 230–236. 41 indexed citations
9.
Sakata, Kenji, et al.. (2018). Regional feature extraction of various fishes based on chemical and microbial variable selection using machine learning. Analytical Methods. 10(18). 2160–2168. 12 indexed citations
10.
Kikuchi, Jun, Kengo Ito, & Yasuhiro Date. (2017). Environmental metabolomics with data science for investigating ecosystem homeostasis. Progress in Nuclear Magnetic Resonance Spectroscopy. 104. 56–88. 32 indexed citations
11.
Osaka, Toshifumi, et al.. (2017). Meta-Analysis of Fecal Microbiota and Metabolites in Experimental Colitic Mice during the Inflammatory and Healing Phases. Nutrients. 9(12). 1329–1329. 91 indexed citations
12.
Date, Yasuhiro & Jun Kikuchi. (2017). Application of a Deep Neural Network to Metabolomics Studies and Its Performance in Determining Important Variables. Analytical Chemistry. 90(3). 1805–1810. 87 indexed citations
13.
Shima, Hideaki, Yasuhiro Date, Amiu Shino, et al.. (2017). Exploring the Impact of Food on the Gut Ecosystem Based on the Combination of Machine Learning and Network Visualization. Nutrients. 9(12). 1307–1307. 16 indexed citations
14.
Uchimiya, Mario, Yuuri Tsuboi, Kengo Ito, Yasuhiro Date, & Jun Kikuchi. (2017). Bacterial Substrate Transformation Tracked by Stable-Isotope-Guided NMR Metabolomics: Application in a Natural Aquatic Microbial Community. Metabolites. 7(4). 52–52. 7 indexed citations
15.
Date, Yasuhiro, et al.. (2016). Visualization of Microfloral Metabolism for Marine Waste Recycling. Metabolites. 6(1). 7–7. 14 indexed citations
16.
Yamazawa, Akira, et al.. (2013). Cellulose Digestion and Metabolism Induced Biocatalytic Transitions in Anaerobic Microbial Ecosystems. Metabolites. 4(1). 36–52. 18 indexed citations
17.
Yamazawa, Akira, et al.. (2013). Solid-, Solution-, and Gas-state NMR Monitoring of 13C-Cellulose Degradation in an Anaerobic Microbial Ecosystem. Molecules. 18(8). 9021–9033. 31 indexed citations
18.
Yamazawa, Akira, et al.. (2013). Visualizing microbial dechlorination processes in underground ecosystem by statistical correlation and network analysis approach. Journal of Bioscience and Bioengineering. 117(3). 305–309. 8 indexed citations
19.
Everroad, R. Craig, Seiji Yoshida, Yuuri Tsuboi, et al.. (2012). Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy. Journal of Visualized Experiments. e3163–e3163. 13 indexed citations
20.
Date, Yasuhiro, Yumiko Nakanishi, Shinji Fukuda, et al.. (2010). New monitoring approach for metabolic dynamics in microbial ecosystems using stable-isotope-labeling technologies. Journal of Bioscience and Bioengineering. 110(1). 87–93. 31 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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