Su‐Fern Tan
Impact in
- Hematology top 10%
- Acute Myeloid Leukemia Research
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- Sphingolipid Metabolism and Signaling
- Lipid Membrane Structure and Behavior
Papers in
- Hematology 13
- Acute Myeloid Leukemia Research 13
-
- Sphingolipid Metabolism and Signaling 25
- Lipid Membrane Structure and Behavior 4
- Histone Deacetylase Inhibitors Research 3
- Co-authors
- David J. Feith (25 shared papers)Thomas P. Loughran (25 shared papers)Myles C. Cabot (16 shared papers)Todd E. Fox (13 shared papers)MARK A. KESTER (15 shared papers)Samy A.F. Morad (7 shared papers)David F. Claxton (12 shared papers)Jennifer M. Pearson (3 shared papers)
- Journals
- Journal of Lipid Research (4 papers)Blood (4 papers)Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids (2 papers)The FASEB Journal (2 papers)Blood Advances (2 papers)
- Partner nations
- United StatesEgyptSpain
In The Last Decade
Su‐Fern Tan
34 papers receiving 773 citations
Peers
Comparison fields: 5 of 69
- Hematology 167
- Molecular Biology 546
- Immunology 141
- Cell Biology 108
- Genetics 60
Countries citing papers authored by Su‐Fern Tan
This map shows the geographic impact of Su‐Fern 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 Su‐Fern Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Su‐Fern Tan more than expected).
Fields of papers citing papers by Su‐Fern Tan
This network shows the impact of papers produced by Su‐Fern 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 Su‐Fern Tan. The network helps show where Su‐Fern Tan may publish in the future.
Co-authors
The 25 scholars most cited alongside Su‐Fern 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 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 83 | |
| 2 | 2010 | 76 | |
| 3 | 2013 | 51 | |
| 4 | 2022 | 49 | |
| 5 | 2019 | 41 | |
| 6 | 2011 | 36 | |
| 7 | 2012 | 34 | |
| 8 | 2019 | 29 | |
| 9 | 2016 | 28 | |
| 10 | 2019 | 28 | |
| 11 | 2017 | 27 | |
| 12 | 2019 | 27 | |
| 13 | 2016 | 26 | |
| 14 | 2017 | 26 | |
| 15 | 2020 | 25 | |
| 16 | 2015 | 25 | |
| 17 | 2017 | 22 | |
| 18 | 2022 | 17 | |
| 19 | 2019 | 17 | |
| 20 | 2018 | 16 |
About Su‐Fern Tan
Su‐Fern Tan is a scholar working on Hematology, Molecular Biology, Immunology, Oncology and Genetics, having authored 35 papers that have together received 779 indexed citations. Recurring topics across this work include Sphingolipid Metabolism and Signaling (25 papers), Acute Myeloid Leukemia Research (13 papers), Drug Transport and Resistance Mechanisms (7 papers), Immune Cell Function and Interaction (7 papers), Lipid Membrane Structure and Behavior (4 papers), Chronic Lymphocytic Leukemia Research (4 papers), Histone Deacetylase Inhibitors Research (3 papers) and Advanced Fluorescence Microscopy Techniques (2 papers). The work is most often cited by research in Hematology (167 citations), Molecular Biology (546 citations), Immunology (141 citations), Cell Biology (108 citations) and Genetics (60 citations). Su‐Fern Tan has collaborated with scholars based in United States, Egypt and Spain. Frequent co-authors include David J. Feith, Thomas P. Loughran, Myles C. Cabot, Todd E. Fox, MARK A. KESTER, Samy A.F. Morad, David F. Claxton, Jennifer M. Pearson, Dhimant H. Desai and Shantu Amin. Their work appears in journals such as Journal of Lipid Research, Blood, Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids, The FASEB Journal and Blood Advances.
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.