Pan Tan
Impact in
Papers in
-
- Protein Structure and Dynamics 11
- RNA and protein synthesis mechanisms 6
- Machine Learning in Bioinformatics 5
- Genomics and Phylogenetic Studies 2
-
- Material Dynamics and Properties 4
- Enzyme Structure and Function 4
- Co-authors
- Liang Hong (20 shared papers)Eugene Mamontov (2 shared papers)Xiangjun Xing (2 shared papers)Qin Xu (1 shared paper)Jinglai Li (2 shared papers)Loukas Petridis (2 shared papers)Lirong Zheng (5 shared papers)Jeremy C. Smith (1 shared paper)
- Journals
- Nature Communications (3 papers)Acta Pharmaceutica Sinica B (2 papers)Journal of Cheminformatics (2 papers)Physical Chemistry Chemical Physics (2 papers)Physical Review Research (2 papers)
- Partner nations
- ChinaUnited StatesNorth Korea
In The Last Decade
Pan Tan
25 papers receiving 300 citations
Peers
Comparison fields: 5 of 82
- Filtration and Separation 6
- Modeling and Simulation 13
- Molecular Biology 154
- Spectroscopy 36
- Atomic and Molecular Physics, and Optics 60
Countries citing papers authored by Pan Tan
This map shows the geographic impact of Pan 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 Pan Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pan Tan more than expected).
Fields of papers citing papers by Pan Tan
This network shows the impact of papers produced by Pan 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 Pan Tan. The network helps show where Pan Tan may publish in the future.
Co-authors
The 25 scholars most cited alongside Pan 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 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 61 | |
| 2 | 2021 | 32 | |
| 3 | 2018 | 26 | |
| 4 | 2024 | 25 | |
| 5 | 2023 | 22 | |
| 6 | 2020 | 17 | |
| 7 | 2024 | 13 | |
| 8 | 2022 | 13 | |
| 9 | 2022 | 12 | |
| 10 | 2022 | 12 | |
| 11 | 2024 | 10 | |
| 12 | 2020 | 10 | |
| 13 | 2020 | 10 | |
| 14 | 2020 | 9 | |
| 15 | 2024 | 7 | |
| 16 | 2025 | 5 | |
| 17 | 2024 | 5 | |
| 18 | 2019 | 5 | |
| 19 | 2020 | 4 | |
| 20 | 2019 | 3 |
About Pan Tan
Pan Tan is a scholar working on Molecular Biology, Materials Chemistry, Atomic and Molecular Physics, and Optics, Biomedical Engineering and Spectroscopy, having authored 27 papers that have together received 312 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (11 papers), RNA and protein synthesis mechanisms (6 papers), Machine Learning in Bioinformatics (5 papers), Spectroscopy and Quantum Chemical Studies (5 papers), Material Dynamics and Properties (4 papers), Enzyme Structure and Function (4 papers), Genomics and Phylogenetic Studies (2 papers) and Nanopore and Nanochannel Transport Studies (2 papers). The work is most often cited by research in Filtration and Separation (6 citations), Modeling and Simulation (13 citations), Molecular Biology (154 citations), Spectroscopy (36 citations) and Atomic and Molecular Physics, and Optics (60 citations). Pan Tan has collaborated with scholars based in China, United States and North Korea. Frequent co-authors include Liang Hong, Eugene Mamontov, Xiangjun Xing, Qin Xu, Jinglai Li, Loukas Petridis, Lirong Zheng, Jeremy C. Smith, Zhuo Liu and Victoria García Sakai. Their work appears in journals such as Nature Communications, Acta Pharmaceutica Sinica B, Journal of Cheminformatics, Physical Chemistry Chemical Physics and Physical Review Research.
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.