Yun Tan

99 total papers · 3.2k total citations
52 papers, 983 citations indexed

About

Yun Tan is a scholar working on Molecular Biology, Immunology and Hematology. According to data from OpenAlex, Yun Tan has authored 52 papers receiving a total of 983 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 15 papers in Immunology and 14 papers in Hematology. Recurrent topics in Yun Tan's work include Acute Myeloid Leukemia Research (14 papers), Retinoids in leukemia and cellular processes (10 papers) and Protein Degradation and Inhibitors (6 papers). Yun Tan is often cited by papers focused on Acute Myeloid Leukemia Research (14 papers), Retinoids in leukemia and cellular processes (10 papers) and Protein Degradation and Inhibitors (6 papers). Yun Tan collaborates with scholars based in China, United States and United Kingdom. Yun Tan's co-authors include Kankan Wang, Yongju Luo, Jiajie Zhu, Sai‐Juan Chen, Hesheng Jiang, Gan Xi, Chao Li, Wen Jin, Hai Fang and Rongsheng Zhang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Yun Tan

50 papers receiving 976 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Yun Tan 514 309 183 158 111 52 983
Peng Liu 420 0.8× 652 2.1× 107 0.6× 130 0.8× 53 0.5× 49 1.2k
Kerstin Lindahl‐Kiessling 408 0.8× 404 1.3× 186 1.0× 77 0.5× 95 0.9× 55 1.2k
Silvia E. Hajos 624 1.2× 251 0.8× 150 0.8× 165 1.0× 78 0.7× 72 1.2k
Liye Chen 363 0.7× 325 1.1× 65 0.4× 152 1.0× 151 1.4× 46 1.0k
Mairi McGrath 288 0.6× 506 1.6× 88 0.5× 169 1.1× 78 0.7× 20 1.1k
Xiaokun Shen 639 1.2× 162 0.5× 320 1.7× 243 1.5× 25 0.2× 43 1.2k
Patricia Lagadec 415 0.8× 235 0.8× 139 0.8× 161 1.0× 70 0.6× 46 1.0k
Akira Shiraishi 364 0.7× 413 1.3× 157 0.9× 261 1.7× 86 0.8× 38 1.0k
Xiaoling Liu 608 1.2× 314 1.0× 282 1.5× 303 1.9× 44 0.4× 55 1.2k
Line Saffar 361 0.7× 226 0.7× 97 0.5× 202 1.3× 56 0.5× 30 838

Countries citing papers authored by Yun Tan

Since Specialization
Citations

This map shows the geographic impact of Yun 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 Yun Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yun Tan more than expected).

Fields of papers citing papers by Yun Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yun 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 Yun Tan. The network helps show where Yun Tan may publish in the future.

Co-authorship network of co-authors of Yun Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Yun Tan. A scholar is included among the top collaborators of Yun Tan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yun Tan. Yun Tan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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.

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