Chae Young

537 total citations
12 papers, 216 citations indexed

About

Chae Young is a scholar working on Molecular Biology, Cardiology and Cardiovascular Medicine and Surgery. According to data from OpenAlex, Chae Young has authored 12 papers receiving a total of 216 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 8 papers in Cardiology and Cardiovascular Medicine and 4 papers in Surgery. Recurrent topics in Chae Young's work include Ion channel regulation and function (9 papers), Cardiac electrophysiology and arrhythmias (8 papers) and Pancreatic function and diabetes (4 papers). Chae Young is often cited by papers focused on Ion channel regulation and function (9 papers), Cardiac electrophysiology and arrhythmias (8 papers) and Pancreatic function and diabetes (4 papers). Chae Young collaborates with scholars based in Japan, South Korea and United Kingdom. Chae Young's co-authors include Akinori Noma, Akira Amano, Yukiko Himeno, Patrik Rorsman, Makoto Shigeto, Kohei Kaku, Yung E. Earm, Trevor Powell, Jianwu Wang and Shinpei Fujimoto and has published in prestigious journals such as PLoS ONE, Biophysical Journal and Journal of Theoretical Biology.

In The Last Decade

Chae Young

12 papers receiving 210 citations

Peers

Chae Young
Joshua R. St. Clair United States
Gery Barmettler Switzerland
Edward Longo United States
W. Schmeer Germany
Shuyan Li China
Henry D. Huang United States
Joshua R. St. Clair United States
Chae Young
Citations per year, relative to Chae Young Chae Young (= 1×) peers Joshua R. St. Clair

Countries citing papers authored by Chae Young

Since Specialization
Citations

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

Fields of papers citing papers by Chae Young

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chae Young

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

All Works

12 of 12 papers shown
1.
Shigeto, Makoto, Chae Young, Patrik Rorsman, & Kohei Kaku. (2017). A role of PLC/PKC-dependent pathway in GLP-1-stimulated insulin secretion. Journal of Molecular Medicine. 95(4). 361–368. 43 indexed citations
2.
Young, Chae, et al.. (2015). Quantitative Decomposition of Dynamics of Mathematical Cell Models: Method and Application to Ventricular Myocyte Models. PLoS ONE. 10(6). e0124970–e0124970. 5 indexed citations
3.
Himeno, Yukiko, Keiichi Asakura, Chae Young, et al.. (2015). A Human Ventricular Myocyte Model with a Refined Representation of Excitation-Contraction Coupling. Biophysical Journal. 109(2). 415–427. 25 indexed citations
4.
Young, Chae & Akinori Noma. (2012). Steady-state solutions of cell volume in a cardiac myocyte model elaborated for membrane excitation, ion homeostasis and Ca2+ dynamics. Journal of Theoretical Biology. 307. 70–81. 4 indexed citations
5.
Young, Chae, et al.. (2011). Time-dependent changes in membrane excitability during glucose-induced bursting activity in pancreatic β cells. The Journal of General Physiology. 138(1). 39–47. 23 indexed citations
6.
Young, Chae, Trevor Powell, & Akinori Noma. (2011). Analyzing electrical activities of pancreatic β cells using mathematical models. Progress in Biophysics and Molecular Biology. 107(2). 265–273. 10 indexed citations
7.
Young, Chae, Yasuhiko Nakamura, Yukiko Himeno, et al.. (2011). Ionic mechanisms and Ca2+ dynamics underlying the glucose response of pancreatic β cells: a simulation study. The Journal of General Physiology. 138(1). 21–37. 47 indexed citations
8.
Young, Chae, et al.. (2010). Characterization of the cardiac Na+/K+ pump by development of a comprehensive and mechanistic model. Journal of Theoretical Biology. 265(1). 68–77. 13 indexed citations
9.
Young, Chae, et al.. (2009). A Model of Na+/H+ Exchanger and Its Central Role in Regulation of pH and Na+ in Cardiac Myocytes. Biophysical Journal. 97(10). 2674–2683. 12 indexed citations
10.
Young, Chae & Akinori Noma. (2009). Modeling the Cardiac Na+/H+ Exchanger Based on Major Experimental Findings. Molecules and Cells. 28(2). 81–86. 1 indexed citations
11.
Young, Chae, et al.. (2009). A Novel Method to Quantify Contribution of Channels and Transporters to Membrane Potential Dynamics. Biophysical Journal. 97(12). 3086–3094. 22 indexed citations
12.
Young, Chae, et al.. (2007). Electrophysiological modelling of pulmonary artery smooth muscle cells in the rabbits—Special consideration to the generation of hypoxic pulmonary vasoconstriction. Progress in Biophysics and Molecular Biology. 96(1-3). 399–420. 11 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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