Song Tan
Impact in
- Molecular Biology top 0.5%
- Genomics and Chromatin Dynamics
- Epigenetics and DNA Methylation
- RNA and protein synthesis mechanisms
- RNA modifications and cancer
- RNA Research and Splicing
- Ubiquitin and proteasome pathways
- DNA Repair Mechanisms
- Cancer-related gene regulation
- Aging top 5%
Papers in
-
- Genomics and Chromatin Dynamics 52
- Epigenetics and DNA Methylation 17
- RNA and protein synthesis mechanisms 15
- RNA Research and Splicing 12
- DNA Repair Mechanisms 11
- Ubiquitin and proteasome pathways 11
- RNA modifications and cancer 11
- Cancer-related gene regulation 11
- Co-authors
- Timothy J. RichmondRobert K. McGintyWilliam SelleckJacques CôtéYannick DoyonWilliam S. LaneLuca PellegriniRavindra D. Makde
- Journals
- Molecular Cell (7 papers)Journal of Molecular Biology (6 papers)Protein Expression and Purification (5 papers)Nature (5 papers)The Astrophysical Journal (4 papers)
- Partner nations
- United StatesChinaCanada
In The Last Decade
Song Tan
109 papers receiving 7.3k citations
Hit Papers
Peers
Comparison fields: 5 of 139
- Molecular Biology 6.4k
- Aging 63
- Structural Biology 34
- Cell Biology 380
- Genetics 649
Countries citing papers authored by Song Tan
This map shows the geographic impact of Song 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 Song Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Song Tan more than expected).
Fields of papers citing papers by Song Tan
This network shows the impact of papers produced by Song 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 Song Tan. The network helps show where Song Tan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Song 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
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 8 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 2 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 10 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 6 | |
| 10 | 2023 | 30 | |
| 11 | 2023 | 10 | |
| 12 | 2023 | 15 | |
| 13 | 2023 | 3 | |
| 14 | 2023 | 7 | |
| 15 | 2023 | 0 | |
| 16 | 2022 | 9 | |
| 17 | 2022 | 38 | |
| 18 | 2022 | 27 | |
| 19 | 2021 | 26 | |
| 20 | 2011 | 0 |
About Song Tan
Song Tan is a scholar working on Molecular Biology, Endocrine and Autonomic Systems, Astronomy and Astrophysics, Gastroenterology and Ecology, Evolution, Behavior and Systematics, having authored 114 papers that have together received 7.4k indexed citations. Recurring topics across this work include Genomics and Chromatin Dynamics (52 papers), Epigenetics and DNA Methylation (17 papers), RNA and protein synthesis mechanisms (15 papers), RNA Research and Splicing (12 papers), DNA Repair Mechanisms (11 papers), Ubiquitin and proteasome pathways (11 papers), RNA modifications and cancer (11 papers) and Cancer-related gene regulation (11 papers). The work is most often cited by research in Molecular Biology (6.4k citations), Aging (63 citations), Structural Biology (34 citations), Cell Biology (380 citations) and Genetics (649 citations). Song Tan has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Timothy J. Richmond, Robert K. McGinty, William Selleck, Jacques Côté, Yannick Doyon, William S. Lane, Luca Pellegrini, Ravindra D. Makde, Ryan C. Henrici and Joseph R. England. Their work appears in journals such as Molecular Cell, Journal of Molecular Biology, Protein Expression and Purification, Nature and The Astrophysical Journal.
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.