Hee Won Lee
Impact in
- Family Practice top 0.5%
- Clinical Reasoning and Diagnostic Skills
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- Carbon dioxide utilization in catalysis
Papers in
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- Receptor Mechanisms and Signaling 2
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- Caching and Content Delivery 5
- Advanced Data Storage Technologies 4
- Co-authors
- Peter J. Pronovost (3 shared papers)David E. Newman‐Toker (2 shared papers)Simon C. Mathews (2 shared papers)Andrew D. Shore (2 shared papers)Ali S. Saber Tehrani (1 shared paper)Martin A. Makary (1 shared paper)Ung Lee (5 shared papers)Da Hye Won (2 shared papers)
- Journals
- BMB Reports (3 papers)BMJ Quality & Safety (2 papers)Experimental & Molecular Medicine (2 papers)Organic Letters (2 papers)Primary Health Care Research & Development (1 paper)
- Partner nations
- South KoreaUnited StatesUnited Kingdom
In The Last Decade
Hee Won Lee
61 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 143
- Family Practice 194
- Process Chemistry and Technology 73
- Catalysis 170
- Health Informatics 24
- Pharmacy 76
Countries citing papers authored by Hee Won Lee
This map shows the geographic impact of Hee Won Lee'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 Hee Won Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hee Won Lee more than expected).
Fields of papers citing papers by Hee Won Lee
This network shows the impact of papers produced by Hee Won Lee. 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 Hee Won Lee. The network helps show where Hee Won Lee may publish in the future.
Co-authors
The 25 scholars most cited alongside Hee Won Lee, 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 67 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 245 | |
| 2 | 2012 | 185 | |
| 3 | 2021 | 141 | |
| 4 | 2022 | 131 | |
| 5 | 2008 | 97 | |
| 6 | 2018 | 77 | |
| 7 | 2019 | 63 | |
| 8 | 2014 | 54 | |
| 9 | 2007 | 39 | |
| 10 | 2009 | 37 | |
| 11 | 2016 | 32 | |
| 12 | 2018 | 29 | |
| 13 | 2014 | 28 | |
| 14 | 2021 | 25 | |
| 15 | 2019 | 25 | |
| 16 | 2018 | 23 | |
| 17 | 2020 | 21 | |
| 18 | 2014 | 19 | |
| 19 | 2019 | 17 | |
| 20 | 2019 | 16 |
About Hee Won Lee
Hee Won Lee is a scholar working on Molecular Biology, Computer Networks and Communications, Pharmacology, Psychiatry and Mental health and Organic Chemistry, having authored 67 papers that have together received 1.5k indexed citations. Recurring topics across this work include Caching and Content Delivery (5 papers), Asymmetric Hydrogenation and Catalysis (4 papers), Carbon dioxide utilization in catalysis (4 papers), Advanced Data Storage Technologies (4 papers), melanin and skin pigmentation (3 papers), Cloud Computing and Resource Management (3 papers), Receptor Mechanisms and Signaling (2 papers) and Catalytic Processes in Materials Science (2 papers). The work is most often cited by research in Family Practice (194 citations), Process Chemistry and Technology (73 citations), Catalysis (170 citations), Health Informatics (24 citations) and Pharmacy (76 citations). Hee Won Lee has collaborated with scholars based in South Korea, United States and United Kingdom. Frequent co-authors include Peter J. Pronovost, David E. Newman‐Toker, Simon C. Mathews, Andrew D. Shore, Ali S. Saber Tehrani, Martin A. Makary, Ung Lee, Da Hye Won, Dongjin Kim and Dong Ki Lee. Their work appears in journals such as BMB Reports, BMJ Quality & Safety, Experimental & Molecular Medicine, Organic Letters and Primary Health Care Research & Development.
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.