Jaegyoon Ahn
Impact in
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- Computational Drug Discovery Methods
- Cancer Research top 10%
- Cancer-related molecular mechanisms research
Papers in
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- Bioinformatics and Genomic Networks 22
- Gene expression and cancer classification 17
- Machine Learning in Bioinformatics 5
- Biomedical Text Mining and Ontologies 5
- RNA Research and Splicing 4
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- Computational Drug Discovery Methods 14
- Co-authors
- Sanghyun Park (17 shared papers)Chihyun Park (15 shared papers)Xinshu Xiao (5 shared papers)Youngmi Yoon (17 shared papers)Jae‐Hyung Lee (3 shared papers)Geonhee Lee (1 shared paper)Yilin Xu (2 shared papers)Chonghui Cheng (2 shared papers)
- Journals
- Bioinformatics (4 papers)BMC Bioinformatics (4 papers)PLoS ONE (4 papers)Scientific Reports (3 papers)Nature Communications (2 papers)
- Partner nations
- South KoreaUnited StatesPuerto Rico
In The Last Decade
Jaegyoon Ahn
48 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 96
- Computational Theory and Mathematics 285
- Cancer Research 206
- Molecular Biology 883
- Hepatology 75
- Health Informatics 9
Countries citing papers authored by Jaegyoon Ahn
This map shows the geographic impact of Jaegyoon Ahn'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 Jaegyoon Ahn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jaegyoon Ahn more than expected).
Fields of papers citing papers by Jaegyoon Ahn
This network shows the impact of papers produced by Jaegyoon Ahn. 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 Jaegyoon Ahn. The network helps show where Jaegyoon Ahn may publish in the future.
Co-authors
The 25 scholars most cited alongside Jaegyoon Ahn, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 52 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 186 | |
| 2 | 2015 | 121 | |
| 3 | 2019 | 106 | |
| 4 | 2015 | 78 | |
| 5 | 2021 | 65 | |
| 6 | 2018 | 55 | |
| 7 | 2020 | 48 | |
| 8 | 2014 | 44 | |
| 9 | 2011 | 43 | |
| 10 | 2014 | 38 | |
| 11 | 2018 | 32 | |
| 12 | 2011 | 31 | |
| 13 | 2017 | 30 | |
| 14 | 2022 | 26 | |
| 15 | 2018 | 24 | |
| 16 | 2006 | 24 | |
| 17 | 2015 | 19 | |
| 18 | 2012 | 18 | |
| 19 | 2018 | 18 | |
| 20 | 2021 | 14 |
About Jaegyoon Ahn
Jaegyoon Ahn is a scholar working on Molecular Biology, Computational Theory and Mathematics, Cancer Research, Oncology and Pulmonary and Respiratory Medicine, having authored 52 papers that have together received 1.2k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (22 papers), Gene expression and cancer classification (17 papers), Computational Drug Discovery Methods (14 papers), Cancer Genomics and Diagnostics (5 papers), Machine Learning in Bioinformatics (5 papers), Biomedical Text Mining and Ontologies (5 papers), RNA Research and Splicing (4 papers) and Cancer Immunotherapy and Biomarkers (4 papers). The work is most often cited by research in Computational Theory and Mathematics (285 citations), Cancer Research (206 citations), Molecular Biology (883 citations), Hepatology (75 citations) and Health Informatics (9 citations). Jaegyoon Ahn has collaborated with scholars based in South Korea, United States and Puerto Rico. Frequent co-authors include Sanghyun Park, Chihyun Park, Xinshu Xiao, Youngmi Yoon, Jae‐Hyung Lee, Geonhee Lee, Yilin Xu, Chonghui Cheng, Min Oh and Xin D. Gao. Their work appears in journals such as Bioinformatics, BMC Bioinformatics, PLoS ONE, Scientific Reports and Nature Communications.
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.