Win D. Cheung
Impact in
- Immunology top 5%
- Galectins and Cancer Biology
- Organic Chemistry top 2%
- Carbohydrate Chemistry and Synthesis
Papers in
-
- Glycosylation and Glycoproteins Research 10
- Machine Learning in Bioinformatics 1
- Metabolism, Diabetes, and Cancer 1
- RNA and protein synthesis mechanisms 1
-
- Carbohydrate Chemistry and Synthesis 8
- Co-authors
- Gerald W. Hart (10 shared papers)Natasha E. Zachara (4 shared papers)Jamey D. Marth (1 shared paper)Wagner B. Dias (3 shared papers)Chad Slawson (2 shared papers)Kaoru Sakabe (2 shared papers)M. Daniel Lane (1 shared paper)Keith Vosseller (1 shared paper)
- Journals
- Journal of Biological Chemistry (7 papers)Current Organic Chemistry (1 paper)Science Signaling (1 paper)Biochemical and Biophysical Research Communications (1 paper)Journal of AOAC International (1 paper)
- Partner nations
- United StatesSouth Korea
In The Last Decade
Win D. Cheung
12 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 87
- Immunology 752
- Organic Chemistry 847
- Molecular Biology 1.6k
- Cell Biology 200
- Nutrition and Dietetics 98
Countries citing papers authored by Win D. Cheung
This map shows the geographic impact of Win D. Cheung'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 Win D. Cheung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Win D. Cheung more than expected).
Fields of papers citing papers by Win D. Cheung
This network shows the impact of papers produced by Win D. Cheung. 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 Win D. Cheung. The network helps show where Win D. Cheung may publish in the future.
Co-authors
The 23 scholars most cited alongside Win D. Cheung, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2004 | 490 | |
| 2 | 2010 | 271 | |
| 3 | 2005 | 240 | |
| 4 | 2008 | 191 | |
| 5 | 2008 | 137 | |
| 6 | 2009 | 125 | |
| 7 | 2002 | 107 | |
| 8 | 2008 | 89 | |
| 9 | 2012 | 69 | |
| 10 | 2004 | 24 | |
| 11 | 2004 | 2 | |
| 12 | 2012 | 1 |
About Win D. Cheung
Win D. Cheung is a scholar working on Molecular Biology, Organic Chemistry, Immunology, Oncology and Cell Biology, having authored 12 papers that have together received 1.7k indexed citations. Recurring topics across this work include Glycosylation and Glycoproteins Research (10 papers), Carbohydrate Chemistry and Synthesis (8 papers), Galectins and Cancer Biology (6 papers), Machine Learning in Bioinformatics (1 paper), Metabolism, Diabetes, and Cancer (1 paper), Trace Elements in Health (1 paper), Drug Transport and Resistance Mechanisms (1 paper) and RNA and protein synthesis mechanisms (1 paper). The work is most often cited by research in Immunology (752 citations), Organic Chemistry (847 citations), Molecular Biology (1.6k citations), Cell Biology (200 citations) and Nutrition and Dietetics (98 citations). Win D. Cheung has collaborated with scholars based in United States and South Korea. Frequent co-authors include Gerald W. Hart, Natasha E. Zachara, Jamey D. Marth, Wagner B. Dias, Chad Slawson, Kaoru Sakabe, M. Daniel Lane, Keith Vosseller, A. Mandal and José Argüello. Their work appears in journals such as Journal of Biological Chemistry, Current Organic Chemistry, Science Signaling, Biochemical and Biophysical Research Communications and Journal of AOAC International.
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.