Sungki Hong

1.6k total citations · 1 hit paper
23 papers, 1.2k citations indexed

About

Sungki Hong is a scholar working on Molecular Biology, Immunology and Electrical and Electronic Engineering. According to data from OpenAlex, Sungki Hong has authored 23 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 5 papers in Immunology and 4 papers in Electrical and Electronic Engineering. Recurrent topics in Sungki Hong's work include PI3K/AKT/mTOR signaling in cancer (8 papers), Immune Cell Function and Interaction (3 papers) and Polyamine Metabolism and Applications (3 papers). Sungki Hong is often cited by papers focused on PI3K/AKT/mTOR signaling in cancer (8 papers), Immune Cell Function and Interaction (3 papers) and Polyamine Metabolism and Applications (3 papers). Sungki Hong collaborates with scholars based in United States, Japan and South Korea. Sungki Hong's co-authors include Ken Inoki, Sei Yoshida, Masako Narita, Simon Tavaré, Manuela Ferreira, Andrew Young, Satoko Arakawa, Shamith Samarajiwa, Lorraine Berry and Takayuki Nakashima and has published in prestigious journals such as Science, Journal of Biological Chemistry and The Journal of Experimental Medicine.

In The Last Decade

Sungki Hong

21 papers receiving 1.2k citations

Hit Papers

Spatial Coupling of mTOR and Autophagy Augments Secretory... 2011 2026 2016 2021 2011 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sungki Hong United States 14 635 309 265 190 119 23 1.2k
Jae Eun Kim South Korea 16 724 1.1× 233 0.8× 275 1.0× 77 0.4× 145 1.2× 42 1.5k
Young‐Sam Lee South Korea 21 928 1.5× 136 0.4× 316 1.2× 108 0.6× 110 0.9× 75 1.5k
Francisco Altamirano United States 26 1.0k 1.6× 152 0.5× 459 1.7× 69 0.4× 181 1.5× 43 2.1k
Johannes Freudenberg United States 23 1.1k 1.8× 235 0.8× 170 0.6× 146 0.8× 54 0.5× 55 1.8k
Zheng Yin United States 17 782 1.2× 308 1.0× 241 0.9× 239 1.3× 89 0.7× 44 1.5k
Steve Wilson United States 19 575 0.9× 164 0.5× 190 0.7× 225 1.2× 159 1.3× 57 1.7k
Siyuan Chen China 16 641 1.0× 127 0.4× 142 0.5× 373 2.0× 92 0.8× 30 1.3k
Ling Xie China 23 706 1.1× 266 0.9× 141 0.5× 171 0.9× 81 0.7× 74 1.7k

Countries citing papers authored by Sungki Hong

Since Specialization
Citations

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

Fields of papers citing papers by Sungki Hong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sungki Hong

This figure shows the co-authorship network connecting the top 25 collaborators of Sungki Hong. A scholar is included among the top collaborators of Sungki 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 Sungki Hong. Sungki Hong 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
2.
Meng, Ziyi, Linkang Zhou, Sungki Hong, et al.. (2023). Myeloid-specific ablation of Basp1 ameliorates diet-induced NASH in mice by attenuating pro-inflammatory signaling. Hepatology. 79(2). 409–424. 8 indexed citations
3.
Yao, Yao, Sungki Hong, Takayuki Ikeda, et al.. (2020). Amino Acids Enhance Polyubiquitination of Rheb and Its Binding to mTORC1 by Blocking Lysosomal ATXN3 Deubiquitinase Activity. Molecular Cell. 80(3). 437–451.e6. 20 indexed citations
4.
Hong, Sungki, Mallory Freeberg, Ting Han, et al.. (2017). LARP1 functions as a molecular switch for mTORC1-mediated translation of an essential class of mRNAs. eLife. 6. 149 indexed citations
5.
Hong, Sungki, et al.. (2017). Effect of relative humidity on preeclampsia. Clinical and Experimental Obstetrics & Gynecology. 44(2). 264–267. 9 indexed citations
6.
Hong, Sungki & Ken Inoki. (2016). Evaluating the mTOR Pathway in Physiological and Pharmacological Settings. Methods in enzymology on CD-ROM/Methods in enzymology. 587. 405–428. 5 indexed citations
7.
Hong, Sungki, Bin Zhao, David B. Lombard, Diane C. Fingar, & Ken Inoki. (2014). Cross-talk between Sirtuin and Mammalian Target of Rapamycin Complex 1 (mTORC1) Signaling in the Regulation of S6 Kinase 1 (S6K1) Phosphorylation. Journal of Biological Chemistry. 289(19). 13132–13141. 78 indexed citations
8.
Zheng, Gang, et al.. (2013). Corticosterone mediates stress‐related increased intestinal permeability in a region‐specific manner. Neurogastroenterology & Motility. 25(2). e127–39. 88 indexed citations
9.
Narita, Masako, Andrew Young, Satoko Arakawa, et al.. (2011). Spatial Coupling of mTOR and Autophagy Augments Secretory Phenotypes. Science. 332(6032). 966–970. 459 indexed citations breakdown →
10.
Yoshida, Sei, Sungki Hong, Tsukasa Suzuki, et al.. (2011). Redox Regulates Mammalian Target of Rapamycin Complex 1 (mTORC1) Activity by Modulating the TSC1/TSC2-Rheb GTPase Pathway. Journal of Biological Chemistry. 286(37). 32651–32660. 126 indexed citations
11.
Hong, Sungki, et al.. (2011). Evaluation of the Nutrient-Sensing mTOR Pathway. Methods in molecular biology. 821. 29–44. 7 indexed citations
12.
Ikenoue, Tsuneo, Sungki Hong, & Ken Inoki. (2009). Chapter 11 Monitoring Mammalian Target of Rapamycin (mTOR) Activity. Methods in enzymology on CD-ROM/Methods in enzymology. 452. 165–180. 57 indexed citations
13.
Hong, Sungki, et al.. (2008). E2F1 and E2F3 activate ATM through distinct mechanisms to promote E1A-induced apoptosis. Cell Cycle. 7(3). 391–400. 12 indexed citations
14.
Hong, Sungki, Raju V. Pusapati, John T. Powers, & D. Gale Johnson. (2006). Oncogenes and the DNA Damage Response: Myc and E2F1 Engage the ATM Signaling Pathway to Activate p53 and Induce Apoptosis. Cell Cycle. 5(8). 801–803. 37 indexed citations
15.
Hong, Sungki, Miodrag Bolić, & Petar M. Djurić. (2004). An Efficient Fixed-Point Implementation of Residual Resampling Scheme for High-Speed Particle Filters. IEEE Signal Processing Letters. 11(5). 482–485. 39 indexed citations
16.
Hong, Sungki, Margaret S. Wooldridge, & Dennis N. Assanis. (2002). Modeling of chemical and mixing effects on methane autoignition under direct-injection, stratified charged conditions. Proceedings of the Combustion Institute. 29(1). 711–718. 15 indexed citations
18.
Hong, Sungki, Derek B. Sant’Angelo, Bonnie N. Dittel, et al.. (1997). The orientation of a T cell receptor to its MHC class II:peptide ligands. The Journal of Immunology. 159(9). 4395–4402. 25 indexed citations
19.
Hong, Sungki, et al.. (1996). Different superantigens interact with distinct sites in the Vbeta domain of a single T cell receptor.. The Journal of Experimental Medicine. 183(4). 1437–1446. 56 indexed citations
20.
Wolff, Christian, Sungki Hong, H von Grafenstein, & Charles A. Janeway. (1993). TCR-CD4 and TCR-TCR interactions as distinctive mechanisms for the induction of increased intracellular calcium in T-cell signalling.. The Journal of Immunology. 151(3). 1337–1345. 24 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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