S Y Tan
Impact in
-
- Hormonal Regulation and Hypertension
- Hormonal and reproductive studies
- Biochemistry top 5%
- Eicosanoids and Hypertension Pharmacology
Papers in
-
- Hormonal Regulation and Hypertension 15
- Hormonal and reproductive studies 12
- Co-authors
- Patrick J. Mulrow (22 shared papers)Shin Suzuki (7 shared papers)Roberto Franco-Saenz (7 shared papers)Beverley E. Pearson Murphy (3 shared papers)John P. Rapp (2 shared papers)Harry S. Margolius (1 shared paper)Nisha Suyien Chandran (4 shared papers)Robert H. Noth (3 shared papers)
- Journals
- The Journal of Clinical Endocrinology & Metabolism (9 papers)Prostaglandins (3 papers)Endocrinology (3 papers)Experimental Biology and Medicine (2 papers)Steroids (2 papers)
- Partner nations
- United StatesSingaporeCanada
In The Last Decade
S Y Tan
72 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 121
- Endocrinology, Diabetes and Metabolism 376
- Biochemistry 162
- Virology 78
- Behavioral Neuroscience 52
- Pharmacology 244
Countries citing papers authored by S Y Tan
This map shows the geographic impact of S Y Tan'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 S Y Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S Y Tan more than expected).
Fields of papers citing papers by S Y Tan
This network shows the impact of papers produced by S Y Tan. 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 S Y Tan. The network helps show where S Y Tan may publish in the future.
Co-authors
The 25 scholars most cited alongside S Y Tan, 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 79 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 1979 | 129 | |
| 2 | 1978 | 92 | |
| 3 | 1992 | 83 | |
| 4 | 1978 | 67 | |
| 5 | 1977 | 57 | |
| 6 | 1980 | 44 | |
| 7 | 1981 | 38 | |
| 8 | 1980 | 37 | |
| 9 | 1980 | 37 | |
| 10 | 1975 | 36 | |
| 11 | 1981 | 34 | |
| 12 | 1978 | 32 | |
| 13 | 1974 | 31 | |
| 14 | 1978 | 31 | |
| 15 | 2021 | 27 | |
| 16 | 1974 | 25 | |
| 17 | 1991 | 24 | |
| 18 | 1981 | 22 | |
| 19 | 1980 | 22 | |
| 20 | 1975 | 21 |
About S Y Tan
S Y Tan is a scholar working on Endocrinology, Diabetes and Metabolism, Molecular Biology, Pharmacology, Cardiology and Cardiovascular Medicine and History, having authored 79 papers that have together received 1.3k indexed citations. Recurring topics across this work include Hormonal Regulation and Hypertension (15 papers), Hormonal and reproductive studies (12 papers), Inflammatory mediators and NSAID effects (10 papers), Eicosanoids and Hypertension Pharmacology (9 papers), Renin-Angiotensin System Studies (6 papers), Medical History and Innovations (6 papers), Neurology and Historical Studies (5 papers) and History of Medicine Studies (5 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (376 citations), Biochemistry (162 citations), Virology (78 citations), Behavioral Neuroscience (52 citations) and Pharmacology (244 citations). S Y Tan has collaborated with scholars based in United States, Singapore and Canada. Frequent co-authors include Patrick J. Mulrow, Shin Suzuki, Roberto Franco-Saenz, Beverley E. Pearson Murphy, John P. Rapp, Harry S. Margolius, Nisha Suyien Chandran, Robert H. Noth, Ellie Choi and I. Antonipillai. Their work appears in journals such as The Journal of Clinical Endocrinology & Metabolism, Prostaglandins, Endocrinology, Experimental Biology and Medicine and Steroids.
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.