Hee‐Kyung Hong

975 total citations
18 papers, 647 citations indexed

About

Hee‐Kyung Hong is a scholar working on Molecular Biology, Cardiology and Cardiovascular Medicine and Cellular and Molecular Neuroscience. According to data from OpenAlex, Hee‐Kyung Hong has authored 18 papers receiving a total of 647 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 7 papers in Cardiology and Cardiovascular Medicine and 6 papers in Cellular and Molecular Neuroscience. Recurrent topics in Hee‐Kyung Hong's work include Ion channel regulation and function (7 papers), Cardiac electrophysiology and arrhythmias (7 papers) and Circadian rhythm and melatonin (6 papers). Hee‐Kyung Hong is often cited by papers focused on Ion channel regulation and function (7 papers), Cardiac electrophysiology and arrhythmias (7 papers) and Circadian rhythm and melatonin (6 papers). Hee‐Kyung Hong collaborates with scholars based in United States, South Korea and Sweden. Hee‐Kyung Hong's co-authors include Joseph Bass, Chiaki Omura, Kathryn Moynihan Ramsey, Clara Bien Peek, Biliana Marcheva, Grant D. Barish, Mark Perelis, Akihiko Taguchi, Su‐Hyun Jo and Matthew J. Schipma and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Journal of Clinical Investigation.

In The Last Decade

Hee‐Kyung Hong

17 papers receiving 640 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hee‐Kyung Hong United States 11 339 278 181 100 76 18 647
Rachel A. Brewer United States 9 430 1.3× 414 1.5× 143 0.8× 147 1.5× 37 0.5× 11 671
Cristine J. Reitz Canada 14 393 1.2× 314 1.1× 163 0.9× 93 0.9× 22 0.3× 21 670
Hidenori Shirai Japan 13 514 1.5× 359 1.3× 234 1.3× 91 0.9× 52 0.7× 18 858
Céline Jouffe Switzerland 10 654 1.9× 379 1.4× 238 1.3× 189 1.9× 77 1.0× 13 960
Işın Çakır United States 12 406 1.2× 353 1.3× 168 0.9× 15 0.1× 46 0.6× 18 784
Pallas Yao United States 6 76 0.2× 214 0.8× 335 1.9× 184 1.8× 39 0.5× 8 602
Oluwaseun Egbejimi United States 8 447 1.3× 437 1.6× 153 0.8× 114 1.1× 50 0.7× 8 680
Svetlana Nikolaeva Russia 10 327 1.0× 218 0.8× 206 1.1× 31 0.3× 34 0.4× 27 659
Emilie Dalbram Denmark 10 149 0.4× 360 1.3× 192 1.1× 23 0.2× 16 0.2× 10 558
Laurie M. Biela United States 12 38 0.1× 269 1.0× 423 2.3× 85 0.8× 67 0.9× 20 689

Countries citing papers authored by Hee‐Kyung Hong

Since Specialization
Citations

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

Fields of papers citing papers by Hee‐Kyung Hong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hee‐Kyung Hong. 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 Hee‐Kyung Hong. The network helps show where Hee‐Kyung Hong may publish in the future.

Co-authorship network of co-authors of Hee‐Kyung Hong

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

All Works

18 of 18 papers shown
1.
Stoiljković, Milan, Hee‐Kyung Hong, Luis Varela, et al.. (2025). Mitofusin 2 controls mitochondrial and synaptic dynamics of suprachiasmatic VIP neurons and related circadian rhythms. Journal of Clinical Investigation. 135(13). 2 indexed citations
2.
Calantone, Nina, Melanie R. McReynolds, Hannah Hudson, et al.. (2022). mTOR regulation of metabolism limits LPS-induced monocyte inflammatory and procoagulant responses. Communications Biology. 5(1). 878–878. 10 indexed citations
3.
Levine, Daniel C., Hee‐Kyung Hong, Jonathan Cedernaes, et al.. (2021). NADH inhibition of SIRT1 links energy state to transcription during time-restricted feeding. Nature Metabolism. 3(12). 1621–1632. 50 indexed citations
4.
Levine, Daniel C., Hee‐Kyung Hong, Benjamin J. Weidemann, et al.. (2020). NAD+ Controls Circadian Reprogramming through PER2 Nuclear Translocation to Counter Aging. Molecular Cell. 78(5). 835–849.e7. 127 indexed citations
5.
Kula-Eversole, Elżbieta, Evrim Yildirim, Daniel C. Levine, et al.. (2020). Phosphatase of Regenerating Liver-1 Selectively Times Circadian Behavior in Darkness via Function in PDF Neurons and Dephosphorylation of TIMELESS. Current Biology. 31(1). 138–149.e5. 12 indexed citations
6.
Marcheva, Biliana, Mark Perelis, Benjamin J. Weidemann, et al.. (2020). A role for alternative splicing in circadian control of exocytosis and glucose homeostasis. Genes & Development. 34(15-16). 1089–1105. 20 indexed citations
7.
Le, Phuong, S. Bornstein, Katherine J. Motyl, et al.. (2019). A novel mouse model overexpressing Nocturnin results in decreased fat mass in male mice. Journal of Cellular Physiology. 234(11). 20228–20239. 10 indexed citations
8.
Hong, Hee‐Kyung, et al.. (2017). A Study on Influencing Factors of Continuous Use Intention by SNS Connection Type and User Psychology. Journal of Digital Contents Society. 18(5). 957–967. 2 indexed citations
9.
Perelis, Mark, Biliana Marcheva, Kathryn Moynihan Ramsey, et al.. (2015). Pancreatic β cell enhancers regulate rhythmic transcription of genes controlling insulin secretion. Science. 350(6261). aac4250–aac4250. 291 indexed citations
10.
Hong, Hee‐Kyung, Seung Ho Lee, Woo Jin Kim, et al.. (2013). Block of hERG K+ channel and prolongation of action potential duration by fluphenazine at submicromolar concentration. European Journal of Pharmacology. 702(1-3). 165–173. 6 indexed citations
11.
Kumar, Vivek, et al.. (2011). Second-generation high-throughput forward genetic screen in mice to isolate subtle behavioral mutants. Proceedings of the National Academy of Sciences. 108(supplement_3). 15557–15564. 21 indexed citations
12.
Hong, Hee‐Kyung, et al.. (2010). Block of the human ether-a-go-go-related gene (hERG) K+ channel by the antidepressant desipramine. Biochemical and Biophysical Research Communications. 394(3). 536–541. 17 indexed citations
13.
Jang, Ji‐Wook, et al.. (2009). Preparation and Characterization of Sponge Using Demineralized Bone Particle. Polymer Korea. 33(2). 104–110. 1 indexed citations
14.
Jo, Su‐Hyun, et al.. (2009). H1 antihistamine drug promethazine directly blocks hERG K+ channel. Pharmacological Research. 60(5). 429–437. 38 indexed citations
15.
Hong, Hee‐Kyung, Weon‐Jong Yoon, Young Ho Kim, Eun-Sook Yoo, & Su‐Hyun Jo. (2009). Inhibition of the Human Ether-a-go-go-related Gene (HERG) K+ Channels by Lindera erythrocarpa. Journal of Korean Medical Science. 24(6). 1089–1089. 4 indexed citations
16.
Jo, Su‐Hyun, et al.. (2008). Clomipramine block of the hERG K+ channel: Accessibility to F656 and Y652. European Journal of Pharmacology. 592(1-3). 19–25. 26 indexed citations
17.
Jo, Su‐Hyun, et al.. (2007). Protriptyline block of the human ether-à-go-go-related gene (HERG) K+ channel. Life Sciences. 82(5-6). 331–340. 7 indexed citations
18.
Jo, Su‐Hyun, et al.. (2007). Maprotiline block of the human ether-a-go-go-related gene (HERG) K+ channel. Archives of Pharmacal Research. 30(4). 453–60. 3 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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