Eric A. Sobie

5.9k total citations
115 papers, 3.4k citations indexed

About

Eric A. Sobie is a scholar working on Cardiology and Cardiovascular Medicine, Molecular Biology and Cellular and Molecular Neuroscience. According to data from OpenAlex, Eric A. Sobie has authored 115 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 90 papers in Cardiology and Cardiovascular Medicine, 87 papers in Molecular Biology and 43 papers in Cellular and Molecular Neuroscience. Recurrent topics in Eric A. Sobie's work include Cardiac electrophysiology and arrhythmias (85 papers), Ion channel regulation and function (56 papers) and Neuroscience and Neural Engineering (32 papers). Eric A. Sobie is often cited by papers focused on Cardiac electrophysiology and arrhythmias (85 papers), Ion channel regulation and function (56 papers) and Neuroscience and Neural Engineering (32 papers). Eric A. Sobie collaborates with scholars based in United States, Italy and Brazil. Eric A. Sobie's co-authors include W. Jonathan Lederer, Long‐Sheng Song, M. Saleet Jafri, Leslie Tung, W. Jonathan Lederer, Keith W. Dilly, Jáder Santos Cruz, Stacey L. McCulle, C. William Balke and Heping Cheng and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Eric A. Sobie

114 papers receiving 3.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Eric A. Sobie United States 32 2.5k 2.3k 1.1k 150 135 115 3.4k
Ye Chen‐Izu United States 36 1.8k 0.7× 3.0k 1.3× 1.0k 1.0× 203 1.4× 140 1.0× 134 4.6k
M. Saleet Jafri United States 30 1.5k 0.6× 2.2k 0.9× 866 0.8× 62 0.4× 90 0.7× 95 2.9k
Leighton T. Izu United States 28 1.5k 0.6× 1.5k 0.7× 620 0.6× 131 0.9× 142 1.1× 72 2.2k
Gary R. Mirams United Kingdom 36 1.8k 0.7× 1.7k 0.8× 502 0.5× 324 2.2× 78 0.6× 96 3.3k
William E. Louch Norway 38 2.9k 1.2× 2.6k 1.1× 826 0.8× 275 1.8× 413 3.1× 150 4.3k
Jeffrey J. Saucerman United States 32 1.4k 0.6× 2.1k 0.9× 362 0.3× 291 1.9× 278 2.1× 88 3.4k
Niels Voigt Germany 38 4.9k 2.0× 2.9k 1.3× 614 0.6× 61 0.4× 210 1.6× 108 6.0k
Olivier Bernus France 28 2.0k 0.8× 723 0.3× 317 0.3× 285 1.9× 129 1.0× 133 2.7k
Tom O’Hara United States 19 1.4k 0.5× 1.1k 0.5× 362 0.3× 146 1.0× 61 0.5× 37 2.0k
Alan Garny United Kingdom 23 899 0.4× 852 0.4× 205 0.2× 162 1.1× 45 0.3× 41 1.6k

Countries citing papers authored by Eric A. Sobie

Since Specialization
Citations

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

Fields of papers citing papers by Eric A. Sobie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eric A. Sobie. 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 Eric A. Sobie. The network helps show where Eric A. Sobie may publish in the future.

Co-authorship network of co-authors of Eric A. Sobie

This figure shows the co-authorship network connecting the top 25 collaborators of Eric A. Sobie. A scholar is included among the top collaborators of Eric A. Sobie 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 Eric A. Sobie. Eric A. Sobie 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
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Patel, Nihir, Rafael Dariolli, Simon Ng, et al.. (2024). HRAS -Mutant Cardiomyocyte Model of Multifocal Atrial Tachycardia. Circulation Arrhythmia and Electrophysiology. 17(4). e012022–e012022. 2 indexed citations
3.
Sobie, Eric A., et al.. (2024). Cell-to-cell heterogeneity in ion channel conductance impacts substrate vulnerability to arrhythmia. American Journal of Physiology-Heart and Circulatory Physiology. 327(1). H242–H254. 1 indexed citations
4.
Wakatsuki, Tetsuro, et al.. (2024). Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments. PLoS Computational Biology. 20(9). e1011806–e1011806. 3 indexed citations
5.
Xiong, Yuguang, Tong Liu, Tong Chen, et al.. (2022). Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity. Scientific Data. 9(1). 18–18. 3 indexed citations
6.
Rahman, Rayees, Jens Hansen, Yuguang Xiong, et al.. (2021). Protein structure–based gene expression signatures. Proceedings of the National Academy of Sciences. 118(19). 5 indexed citations
7.
Alves, Chrystian Junqueira, Rafael Dariolli, Sangjo Kang, et al.. (2021). Plexin-B2 orchestrates collective stem cell dynamics via actomyosin contractility, cytoskeletal tension and adhesion. Nature Communications. 12(1). 6019–6019. 28 indexed citations
8.
Hasselt, J. G. Coen van, Rayees Rahman, Jens Hansen, et al.. (2020). Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity. Nature Communications. 11(1). 4809–4809. 28 indexed citations
9.
Hung, Ling‐Hong, et al.. (2019). Holistic optimization of an RNA-seq workflow for multi-threaded environments. Bioinformatics. 35(20). 4173–4175. 3 indexed citations
10.
Mei, Xueyan, et al.. (2019). Combining Systems Pharmacology Modeling with Machine Learning to Identify Sub-Populations at Risk of Arrhythmia. Biophysical Journal. 116(3). 230a–230a. 1 indexed citations
11.
Krogh‐Madsen, Trine, et al.. (2016). Population-Based Mathematical Modeling Facilitates the Interpretation of Dynamic Clamp Experiments in Cardiomyocytes. Biophysical Journal. 110(3). 585a–585a. 1 indexed citations
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Severi, Stefano, et al.. (2014). Comprehensive Analyses of Ventricular Myocyte Models Identify Targets Exhibiting Favorable Rate Dependence. PLoS Computational Biology. 10(3). e1003543–e1003543. 32 indexed citations
14.
Chikando, Aristide C., et al.. (2011). Stochastic simulation of cardiac ventricular myocyte calcium dynamics and waves. PubMed. 91. 4677–4680. 3 indexed citations
15.
Sobie, Eric A., et al.. (2010). Spontaneous Ca 2+ sparks and Ca 2+ homeostasis in a minimal model of permeabilized ventricular myocytes. American Journal of Physiology-Heart and Circulatory Physiology. 299(6). H1996–H2008. 11 indexed citations
16.
Sobie, Eric A., et al.. (2009). Regression Analysis for Constraining Free Parameters in Electrophysiological Models of Ventricular Cells. Biophysical Journal. 96(3). 667a–667a. 1 indexed citations
17.
Williams, George S.B., Marco A. Huertas, Eric A. Sobie, M. Saleet Jafri, & Gregory D. Smith. (2007). A Probability Density Approach to Modeling Local Control of Calcium-Induced Calcium Release in Cardiac Myocytes. Biophysical Journal. 92(7). 2311–2328. 45 indexed citations
18.
Sobie, Eric A., et al.. (2005). The Ca2+ leak paradox and “rogue ryanodine receptors”: SR Ca2+ efflux theory and practice. Progress in Biophysics and Molecular Biology. 90(1-3). 172–185. 99 indexed citations
19.
Sobie, Eric A., Long‐Sheng Song, & W. Jonathan Lederer. (2005). Local recovery of Ca2+ release in rat ventricular myocytes. The Journal of Physiology. 565(2). 441–447. 78 indexed citations
20.
Sobie, Eric A., et al.. (1996). Mechanisms of cardiac cell excitation with premature monophasic and biphasic field stimuli: a model study. Biophysical Journal. 70(3). 1347–1362. 20 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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