Yung‐Keun Kwon
- Molecular Biology
- Management Science and Operations Research top 5%
- Artificial Intelligence top 10%
- Computational Theory and Mathematics top 5%
- Genetics
- Co-authors
- Kwang‐Hyun ChoDuc‐Hau LeByung-Ro MoonDongsan KimJunil KimJ. S. Heslop‐HarrisonJeong‐Rae KimJong-Myon Kim
- Topics
- Gene Regulatory Network Analysis (30 papers)Bioinformatics and Genomic Networks (29 papers)Microbial Metabolic Engineering and Bioproduction (9 papers)
- Journals
- SHILAP Revista de lepidopterologíaBioinformaticsPLoS ONE
- Partner nations
- South KoreaVietnamPuerto Rico
In The Last Decade
Yung‐Keun Kwon
49 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 127
- Molecular Biology 680
- Management Science and Operations Research 157
- Artificial Intelligence 119
- Computational Theory and Mathematics 101
- Genetics 89
Countries citing papers authored by Yung‐Keun Kwon
This map shows the geographic impact of Yung‐Keun 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 Yung‐Keun Kwon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yung‐Keun Kwon more than expected).
Fields of papers citing papers by Yung‐Keun Kwon
This network shows the impact of papers produced by Yung‐Keun 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 Yung‐Keun Kwon. The network helps show where Yung‐Keun Kwon may publish in the future.
Co-authorship network of co-authors of Yung‐Keun Kwon
This figure shows the co-authorship network connecting the top 25 collaborators of Yung‐Keun Kwon. A scholar is included among the top collaborators of Yung‐Keun Kwon 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 Yung‐Keun Kwon. Yung‐Keun Kwon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 6 | |
| 3 | 4 | |
| 4 | 6 | |
| 5 | 43 | |
| 6 | 1 | |
| 7 | 4 | |
| 8 | 3 | |
| 9 | 11 | |
| 10 | 8 | |
| 11 | 45 | |
| 12 | 43 | |
| 13 | 48 | |
| 14 | 75 | |
| 15 | 18 | |
| 16 | 132 | |
| 17 | 45 | |
| 18 | 41 | |
| 19 | A Genetic Hybrid For Critical Heat Flux Function Approximation | 1 |
| 20 | Personalized email marketing with a genetic programming circuit model | 7 |
About Yung‐Keun Kwon
Yung‐Keun Kwon is a scholar working on Molecular Biology, Computational Theory and Mathematics and Biophysics, having authored 50 papers that have together received 1.1k indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (30 papers), Bioinformatics and Genomic Networks (29 papers) and Microbial Metabolic Engineering and Bioproduction (9 papers). The work is most often cited by research in Management Science and Operations Research (157 citations), Molecular Biology (680 citations) and Biophysics (41 citations). Yung‐Keun Kwon has collaborated with scholars based in South Korea, Vietnam and Puerto Rico. Frequent co-authors include Kwang‐Hyun Cho, Duc‐Hau Le, Byung-Ro Moon, Dongsan Kim, Junil Kim, J. S. Heslop‐Harrison, Jeong‐Rae Kim, Jong-Myon Kim, Myeongsu Kang and Cheol Hong Kim. Their work appears in journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.
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.