Kai Tan
- Aging top 2%
- Molecular Biology top 2%
- Genomics and Chromatin Dynamics 24
- Single-cell and spatial transcriptomics 17
- Epigenetics and DNA Methylation 14
- Bioinformatics and Genomic Networks 12
- RNA and protein synthesis mechanisms 8
- Genomics and Phylogenetic Studies 6
- Immunology top 5%
- Hematology top 5%
- Cancer Research top 5%
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- CAR-T cell therapy research 9
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- Zebrafish Biomedical Research Applications 8
- Co-authors
- Bing HeChangya ChenLi TengLong GaoQin ZhuNancy A. SpeckPeng GaoDuygu Ucar
- Cited by
- AgingMolecular BiologyImmunology
- Journals
- Blood (6 papers)Nature Communications (5 papers)Proceedings of the National Academy of Sciences (5 papers)
- Partner nations
- United StatesChinaSlovakia
In The Last Decade
Kai Tan
92 papers receiving 3.9k citations
Hit Papers
Peers
Comparison fields: 5 of 138
- Aging 147
- Molecular Biology 2.8k
- Immunology 686
- Hematology 352
- Cancer Research 389
Countries citing papers authored by Kai Tan
This map shows the geographic impact of Kai 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 Kai Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Tan more than expected).
Fields of papers citing papers by Kai Tan
This network shows the impact of papers produced by Kai 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 Kai Tan. The network helps show where Kai Tan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kai 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 | 0 | |
| 2 | 2023 | 25 | |
| 3 | 2023 | 14 | |
| 4 | 2022 | 2 | |
| 5 | 2022 | 13 | |
| 6 | 2022 | 11 | |
| 7 | 2022 | 28 | |
| 8 | 2021 | 124 | |
| 9 | 2021 | 15 | |
| 10 | 2021 | 27 | |
| 11 | 2021 | 22 | |
| 12 | 2021 | 3 | |
| 13 | 2020 | 50 | |
| 14 | 2020 | 107 | |
| 15 | 2019 | 17 | |
| 16 | 2019 | 149 | |
| 17 | A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolutionbreakdown → | 2019 | 301 |
| 18 | 2017 | 14 | |
| 19 | 2012 | 41 | |
| 20 | 2008 | 49 |
About Kai Tan
Kai Tan is a scholar working on Molecular Biology, Hematology, Immunology, Cancer Research and Oncology, having authored 98 papers that have together received 3.9k indexed citations. Recurring topics across this work include Genomics and Chromatin Dynamics (24 papers), Single-cell and spatial transcriptomics (17 papers), Epigenetics and DNA Methylation (14 papers), Bioinformatics and Genomic Networks (12 papers), CAR-T cell therapy research (9 papers), Zebrafish Biomedical Research Applications (8 papers), RNA and protein synthesis mechanisms (8 papers) and Genomics and Phylogenetic Studies (6 papers). The work is most often cited by research in Aging (147 citations), Molecular Biology (2.8k citations), Immunology (686 citations), Hematology (352 citations) and Cancer Research (389 citations). Kai Tan has collaborated with scholars based in United States, China and Slovakia. Frequent co-authors include Bing He, Changya Chen, Li Teng, Long Gao, Qin Zhu, Nancy A. Speck, Peng Gao, Duygu Ucar, Tao Peng and Hiram Firpi. Their work appears in journals such as Blood, Nature Communications, Proceedings of the National Academy of Sciences, Nucleic Acids Research and Genes & Development.
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.