Sunyoung Kwon

26 papers receiving 567 citations

Peers

Sunyoung Kwon
Comparison fields: 5 of 121
  • Computational Theory and Mathematics 158
  • Dermatology 50
  • Immunology and Allergy 29
  • Cancer Research 69
  • Molecular Biology 238
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Huba Kiss Hungary
Yonghong Tang China
Ngan Nguyen Taiwan
Petr Hájek Czechia
Sarah Gul Pakistan
Yanjing Wang China
Cláudia Barbosa Ladeira de Campos Brazil
Nitish K. Mishra United States
Xiaodong Luo United States
Emmett Sprecher United States
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Citations per year

Countries citing papers authored by Sunyoung Kwon

Since Specialization
Citations

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

Fields of papers citing papers by Sunyoung Kwon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Sunyoung Kwon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sunyoung Kwon Line = papers co-authored together Sunyoung Kwon links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019171
2 201881
3 201858
4 202151
5 201449
6 201736
7 201322
8 202215
9 202315
10 202414
11 201814
12 20139
13 20208
14
Clinical and Prognostic Value of Human Mammary Tumor Virus in Korean Patients with Breast Carcinoma.
20196
15 20186
16 20145
17 20194
18
METTL3 regulates alternative splicing of cell cycle-related genes via crosstalk between mRNA m6A modifications and splicing factors.
20234
19 20144
20 20203

About Sunyoung Kwon

Sunyoung Kwon is a scholar working on Molecular Biology, Artificial Intelligence, Cancer Research, Surgery and Information Systems, having authored 32 papers that have together received 585 indexed citations. Recurring topics across this work include Breast Cancer Treatment Studies (3 papers), Computational Drug Discovery Methods (3 papers), Analytical Chemistry and Chromatography (2 papers), EEG and Brain-Computer Interfaces (2 papers), Cancer-related molecular mechanisms research (2 papers), Diverse Approaches in Healthcare and Education Studies (2 papers), Advanced Graph Neural Networks (2 papers) and Cancer-related gene regulation (2 papers). The work is most often cited by research in Computational Theory and Mathematics (158 citations), Dermatology (50 citations), Immunology and Allergy (29 citations), Cancer Research (69 citations) and Molecular Biology (238 citations). Sunyoung Kwon has collaborated with scholars based in South Korea, United States and Ethiopia. Frequent co-authors include Sungroh Yoon, Ho Bae, Byunghan Lee, Jaekoo Lee, Jung Im Na, Jaehyuk Choi, Jung Won Shin, Chang‐Hun Huh, Kyungho Park and Minquan Du. Their work appears in journals such as IEEE Access, Bioinformatics, BMC Bioinformatics, Clinical Cancer Research and IEEE/ACM Transactions on Computational Biology and Bioinformatics.

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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