Sunghoi Hong

2.6k total citations · 1 hit paper
63 papers, 2.1k citations indexed

About

Sunghoi Hong is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Biomedical Engineering. According to data from OpenAlex, Sunghoi Hong has authored 63 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 53 papers in Molecular Biology, 23 papers in Cellular and Molecular Neuroscience and 9 papers in Biomedical Engineering. Recurrent topics in Sunghoi Hong's work include Pluripotent Stem Cells Research (18 papers), Genetic Neurodegenerative Diseases (13 papers) and Extracellular vesicles in disease (12 papers). Sunghoi Hong is often cited by papers focused on Pluripotent Stem Cells Research (18 papers), Genetic Neurodegenerative Diseases (13 papers) and Extracellular vesicles in disease (12 papers). Sunghoi Hong collaborates with scholars based in South Korea, United States and Japan. Sunghoi Hong's co-authors include Hyesun Jeong, Yeonho Choi, Hyunku Shin, Jaena Park, Hyun Koo Kim, Byeong Hyeon Choi, Seongman Kang, Ole Isacson, Kwang Soo Kim and Yong Park and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and ACS Nano.

In The Last Decade

Sunghoi Hong

62 papers receiving 2.1k citations

Hit Papers

Early-Stage Lung Cancer Diagnosis by Deep Learning-Based ... 2020 2026 2022 2024 2020 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Early-Stage Lung Cancer Diagnosis by Deep Learning-Based Spectroscopic Analysis of Circulating Exosomes
    2020 387 citations Hyunku Shin, Byeong Hyeon Choi et al. profile →

Peers

Sunghoi Hong
Jeong Won Park South Korea
Ethan S. Lippmann United States
Do Won Hwang South Korea
Laura Lovato Italy
Jonathan V. Rocheleau Canada
Ronald A. Li United States
Vittorio de Franciscis Italy
Haodi Wu United States
Gene T. Yocum United States
Kai S. Erdmann Germany
Jeong Won Park South Korea
Sunghoi Hong
Citations per year, relative to Sunghoi Hong Sunghoi Hong (= 1×) peers Jeong Won Park

Countries citing papers authored by Sunghoi Hong

Since Specialization
Citations

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

Fields of papers citing papers by Sunghoi Hong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sunghoi Hong

This figure shows the co-authorship network connecting the top 25 collaborators of Sunghoi Hong. A scholar is included among the top collaborators of Sunghoi 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 Sunghoi Hong. Sunghoi 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
1.
Hong, Sunghoi, et al.. (2024). Integrating Artificial Intelligence to Biomedical Science: New Applications for Innovative Stem Cell Research and Drug Development. SHILAP Revista de lepidopterología. 12(7). 95–95. 9 indexed citations
2.
Kang, Ka‐Won, Jeong‐An Gim, Sunghoi Hong, et al.. (2024). Use of extracellular vesicle microRNA profiles in patients with acute myeloid leukemia for the identification of novel biomarkers. PLoS ONE. 19(8). e0306962–e0306962. 5 indexed citations
3.
Hong, Sunghoi, et al.. (2023). Prediction of Stem Cell State Using Cell Image‐Based Deep Learning. SHILAP Revista de lepidopterología. 5(7). 6 indexed citations
4.
Hong, Sunghoi, et al.. (2023). Prediction of Stem Cell State Using Cell Image‐Based Deep Learning. Advanced Intelligent Systems. 5(7). 5 indexed citations
5.
Choi, Byoung Geol, Seung‐Woon Rha, Min Woo Lee, et al.. (2023). Current Status of Cardiovascular Disease According to the Duration of Hypertension in Korean Adults. Global Heart. 18(1). 25–25.
6.
Choi, Kyungah, In-Sik Hwang, Minji Park, et al.. (2022). OCT4-induced oligodendrocyte progenitor cells promote remyelination and ameliorate disease. npj Regenerative Medicine. 7(1). 4–4. 13 indexed citations
7.
Kang, Ka‐Won, Byeong Hyeon Choi, Hyesun Jeong, et al.. (2021). Dual size-exclusion chromatography for efficient isolation of extracellular vesicles from bone marrow derived human plasma. Scientific Reports. 11(1). 217–217. 18 indexed citations
8.
Kang, Ka‐Won, Woojune Hur, Hyunku Shin, et al.. (2020). A Proteomic Approach to Understand the Clinical Significance of Acute Myeloid Leukemia–Derived Extracellular Vesicles Reflecting Essential Characteristics of Leukemia. Molecular & Cellular Proteomics. 20. 100017–100017. 12 indexed citations
9.
Choi, Yeonho, et al.. (2017). Stem cell transplantation for Huntington’s diseases. Methods. 133. 104–112. 24 indexed citations
10.
Kang, Aram, Do‐Young Choi, Kwang Pyo Kim, et al.. (2015). A key lysine residue in the AXH domain of ataxin-1 is essential for its ubiquitylation. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1854(5). 356–364. 14 indexed citations
11.
Park, Hang-Soo, et al.. (2015). Spatio-temporally controlled transfection by quantitative injection into a single cell. Biomaterials. 67. 225–231. 4 indexed citations
12.
Hwang, In-Sik, et al.. (2014). Stem cell therapy and cellular engineering for treatment of neuronal dysfunction in Huntington's disease. Biotechnology Journal. 9(7). 882–894. 10 indexed citations
13.
14.
Hong, Sunghoi, et al.. (2011). Ataxin-1 occupies the promoter region of E-cadherin in vivo and activates CtBP2-repressed promoter. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research. 1813(5). 713–722. 23 indexed citations
15.
Hong, Sunghoi, et al.. (2008). UbcH6 interacts with and ubiquitinates the SCA1 gene product ataxin-1. Biochemical and Biophysical Research Communications. 371(2). 256–260. 18 indexed citations
16.
Hong, Sunghoi, Dong‐Youn Hwang, Soonsang Yoon, et al.. (2007). Functional Analysis of Various Promoters in Lentiviral Vectors at Different Stages of In Vitro Differentiation of Mouse Embryonic Stem Cells. Molecular Therapy. 15(9). 1630–1639. 110 indexed citations
17.
18.
Hong, Seok Jong, Youngbuhm Huh, Han Chae, et al.. (2006). GATA‐3 regulates the transcriptional activity of tyrosine hydroxylase by interacting with CREB. Journal of Neurochemistry. 98(3). 773–781. 31 indexed citations
19.
Hong, Sunghoi, et al.. (2003). p80 coilin, a coiled body-specific protein, interacts with ataxin-1, the SCA1 gene product. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. 1638(1). 35–42. 20 indexed citations
20.
Hong, Sunghoi, et al.. (2002). USP7, a Ubiquitin-Specific Protease, Interacts with Ataxin-1, the SCA1 Gene Product. Molecular and Cellular Neuroscience. 20(2). 298–306. 53 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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