Shinsuke Uda

1.1k total citations
27 papers, 694 citations indexed

About

Shinsuke Uda is a scholar working on Molecular Biology, Computational Theory and Mathematics and Surgery. According to data from OpenAlex, Shinsuke Uda has authored 27 papers receiving a total of 694 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 4 papers in Computational Theory and Mathematics and 3 papers in Surgery. Recurrent topics in Shinsuke Uda's work include Gene Regulatory Network Analysis (13 papers), Bioinformatics and Genomic Networks (8 papers) and Computational Drug Discovery Methods (4 papers). Shinsuke Uda is often cited by papers focused on Gene Regulatory Network Analysis (13 papers), Bioinformatics and Genomic Networks (8 papers) and Computational Drug Discovery Methods (4 papers). Shinsuke Uda collaborates with scholars based in Japan, United States and Australia. Shinsuke Uda's co-authors include Shinya Kuroda, Hiroyuki Kubota, Yu Toyoshima, Takeshi Saito, Yasunori Komori, Takaho Tsuchiya, Jaehoon Chung, Kanako Watanabe, Kazuhiro Fujita and Wataru Ogawa and has published in prestigious journals such as Science, Nature Communications and Molecular Cell.

In The Last Decade

Shinsuke Uda

27 papers receiving 692 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shinsuke Uda Japan 17 499 73 68 61 57 27 694
Noritaka Masaki Japan 17 598 1.2× 67 0.9× 32 0.5× 79 1.3× 34 0.6× 33 1.0k
Alice Ly Germany 16 794 1.6× 220 3.0× 47 0.7× 55 0.9× 39 0.7× 21 1.2k
Santos Carvajal‐Gonzalez United States 18 208 0.4× 52 0.7× 41 0.6× 81 1.3× 95 1.7× 25 771
David R.L. Scriven Canada 19 727 1.5× 282 3.9× 101 1.5× 63 1.0× 65 1.1× 37 1.1k
Yasunori Komori Japan 9 472 0.9× 29 0.4× 64 0.9× 27 0.4× 32 0.6× 12 669
Steve Rees United Kingdom 6 257 0.5× 97 1.3× 19 0.3× 56 0.9× 30 0.5× 9 495
Marco Berrera Switzerland 18 867 1.7× 100 1.4× 16 0.2× 71 1.2× 34 0.6× 27 1.1k
Bruna V. Jardim‐Perassi Brazil 18 356 0.7× 24 0.3× 20 0.3× 95 1.6× 32 0.6× 28 1.0k
Georges Christé France 16 541 1.1× 207 2.8× 32 0.5× 35 0.6× 41 0.7× 45 771
Joanne Layland United Kingdom 14 704 1.4× 73 1.0× 22 0.3× 224 3.7× 76 1.3× 18 1.4k

Countries citing papers authored by Shinsuke Uda

Since Specialization
Citations

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

Fields of papers citing papers by Shinsuke Uda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shinsuke Uda

This figure shows the co-authorship network connecting the top 25 collaborators of Shinsuke Uda. A scholar is included among the top collaborators of Shinsuke Uda 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 Shinsuke Uda. Shinsuke Uda 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.
Ito, Yuki, Shinsuke Uda, Akiyoshi Hirayama, et al.. (2023). Comparison of hepatic responses to glucose perturbation between healthy and obese mice based on the edge type of network structures. Scientific Reports. 13(1). 4758–4758. 1 indexed citations
2.
Fujii, Masashi, Ken‐ichi Hironaka, Shinsuke Uda, et al.. (2022). Four features of temporal patterns characterize similarity among individuals and molecules by glucose ingestion in humans. npj Systems Biology and Applications. 8(1). 6–6. 2 indexed citations
3.
Matsuzaki, Fumiko, Shinsuke Uda, Masaki Matsumoto, et al.. (2021). An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver. Cell Reports. 36(8). 109569–109569. 8 indexed citations
4.
Hironaka, Ken‐ichi, Masashi Fujii, Miki Eto, et al.. (2020). Single-Cell Information Analysis Reveals That Skeletal Muscles Incorporate Cell-to-Cell Variability as Information Not Noise. Cell Reports. 32(9). 108051–108051. 16 indexed citations
5.
Uda, Shinsuke. (2020). Application of information theory in systems biology. Biophysical Reviews. 12(2). 377–384. 29 indexed citations
6.
Fujii, Masashi, Yohei Murakami, Masanori Koyama, et al.. (2019). Logical design of oral glucose ingestion pattern minimizing blood glucose in humans. npj Systems Biology and Applications. 5(1). 31–31. 7 indexed citations
7.
Ohigashi, Izumi, Yu Tanaka, Kenta Kondo, et al.. (2019). Trans-omics Impact of Thymoproteasome in Cortical Thymic Epithelial Cells. Cell Reports. 29(9). 2901–2916.e6. 24 indexed citations
8.
Kubota, Hiroyuki, et al.. (2018). In Vivo Decoding Mechanisms of the Temporal Patterns of Blood Insulin by the Insulin-AKT Pathway in the Liver. Cell Systems. 7(1). 118–128.e3. 17 indexed citations
9.
Fujii, Masashi, Shinsuke Uda, Hiroyuki Kubota, et al.. (2018). Increase in hepatic and decrease in peripheral insulin clearance characterize abnormal temporal patterns of serum insulin in diabetic subjects. npj Systems Biology and Applications. 4(1). 14–14. 15 indexed citations
10.
Kawata, Kentaro, Atsushi Hatano, Katsuyuki Yugi, et al.. (2018). Trans-omic Analysis Reveals Selective Responses to Induced and Basal Insulin across Signaling, Transcriptional, and Metabolic Networks. iScience. 7. 212–229. 28 indexed citations
11.
Tsuchiya, Takaho, Masashi Fujii, Naoki Matsuda, et al.. (2017). System identification of signaling dependent gene expression with different time-scale data. PLoS Computational Biology. 13(12). e1005913–e1005913. 4 indexed citations
12.
Kudo, Takamasa, Shinsuke Uda, Takaho Tsuchiya, et al.. (2016). Laguerre Filter Analysis with Partial Least Square Regression Reveals a Priming Effect of ERK and CREB on c-FOS Induction. PLoS ONE. 11(8). e0160548–e0160548. 2 indexed citations
13.
Uda, Shinsuke & Shinya Kuroda. (2015). Analysis of cellular signal transduction from an information theoretic approach. Seminars in Cell and Developmental Biology. 51. 24–31. 23 indexed citations
14.
Yugi, Katsuyuki, Hiroyuki Kubota, Yu Toyoshima, et al.. (2014). Reconstruction of Insulin Signal Flow from Phosphoproteome and Metabolome Data. Cell Reports. 8(4). 1171–1183. 66 indexed citations
15.
Saito, Takeshi, et al.. (2013). Temporal Decoding of MAP Kinase and CREB Phosphorylation by Selective Immediate Early Gene Expression. PLoS ONE. 8(3). e57037–e57037. 19 indexed citations
16.
Kubota, Hiroyuki, Yu Toyoshima, Shinsuke Uda, et al.. (2012). Temporal Coding of Insulin Action through Multiplexing of the AKT Pathway. Molecular Cell. 46(6). 820–832. 90 indexed citations
17.
Toyoshima, Yu, et al.. (2012). Sensitivity control through attenuation of signal transfer efficiency by negative regulation of cellular signalling. Nature Communications. 3(1). 743–743. 16 indexed citations
18.
Watanabe, Kanako, Katsuyuki Yugi, Shinsuke Uda, et al.. (2012). Latent process genes for cell differentiation are common decoders of neurite extension length. Journal of Cell Science. 125(Pt 9). 2198–211. 22 indexed citations
19.
Chung, Jaehoon, et al.. (2010). Timing-Dependent Actions of NGF Required for Cell Differentiation. PLoS ONE. 5(2). e9011–e9011. 43 indexed citations
20.
Uda, Shinsuke, et al.. (2010). A Quantitative Image Cytometry Technique for Time Series or Population Analyses of Signaling Networks. PLoS ONE. 5(4). e9955–e9955. 21 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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