Ming Yan

8.8k total citations · 1 hit paper
166 papers, 6.2k citations indexed

About

Ming Yan is a scholar working on Molecular Biology, Immunology and Cancer Research. According to data from OpenAlex, Ming Yan has authored 166 papers receiving a total of 6.2k indexed citations (citations by other indexed papers that have themselves been cited), including 94 papers in Molecular Biology, 49 papers in Immunology and 45 papers in Cancer Research. Recurrent topics in Ming Yan's work include Acute Myeloid Leukemia Research (25 papers), Cancer-related molecular mechanisms research (24 papers) and RNA modifications and cancer (22 papers). Ming Yan is often cited by papers focused on Acute Myeloid Leukemia Research (25 papers), Cancer-related molecular mechanisms research (24 papers) and RNA modifications and cancer (22 papers). Ming Yan collaborates with scholars based in China, United States and Japan. Ming Yan's co-authors include Dong‐Er Zhang, Qin Xu, Jianjun Zhang, Wantao Chen, Luke F. Peterson, Xing Qin, Keun Il Kim, Wantao Chen, Kenneth J. Ritchie and Oxana A. Malakhova and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Journal of Clinical Investigation.

In The Last Decade

Ming Yan

165 papers receiving 6.2k citations

Hit Papers

Exosomal miR-196a derived... 2019 2026 2021 2023 2019 100 200 300

Author Peers

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

Author Last Decade Papers Cites
Ming Yan 3.8k 1.7k 1.6k 1.3k 786 166 6.2k
José L. Fernández-Luna 3.1k 0.8× 2.3k 1.3× 834 0.5× 2.0k 1.5× 973 1.2× 94 6.8k
Susheela Tridandapani 3.1k 0.8× 3.0k 1.7× 742 0.5× 1.1k 0.8× 343 0.4× 115 6.4k
Eberhard Gunsilius 1.6k 0.4× 1.4k 0.8× 555 0.4× 1.6k 1.2× 786 1.0× 135 4.6k
Preet M. Chaudhary 3.4k 0.9× 1.9k 1.1× 1.2k 0.8× 2.8k 2.1× 375 0.5× 88 6.4k
Salvatore Venuta 2.2k 0.6× 1.4k 0.8× 757 0.5× 1.6k 1.2× 656 0.8× 137 5.1k
Chunhong Ma 2.7k 0.7× 2.9k 1.7× 930 0.6× 1.4k 1.1× 269 0.3× 193 6.7k
Akifumi Takaori‐Kondo 2.3k 0.6× 1.9k 1.1× 458 0.3× 1.2k 0.9× 678 0.9× 288 5.8k
Ken Schooley 3.3k 0.9× 3.3k 1.9× 913 0.6× 996 0.8× 503 0.6× 17 6.5k
Thomas Winkler 3.4k 0.9× 4.6k 2.7× 758 0.5× 1.4k 1.1× 1.4k 1.8× 210 10.0k
Valéry Combes 2.7k 0.7× 1.6k 1.0× 1.0k 0.6× 465 0.4× 626 0.8× 87 5.5k

Countries citing papers authored by Ming Yan

Since Specialization
Citations

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

Fields of papers citing papers by Ming Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Yan. A scholar is included among the top collaborators of Ming Yan 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 Ming Yan. Ming Yan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yang, Wenyi, Zhaoyang Guo, Houyu Ju, et al.. (2025). IL-4-STAT6-induced high Siglec-G/10 expression aggravates the severe immune suppressive tumor microenvironment and impedes the efficacy of immunotherapy in head and neck squamous cell carcinoma. Journal for ImmunoTherapy of Cancer. 13(6). e011474–e011474. 2 indexed citations
2.
Yin, Yu, Hansheng Chen, Guanyu Deng, et al.. (2025). A self-optimized alloy with multi-scale hierarchical microstructure and enhanced mechanical properties. Materials & Design. 250. 113620–113620. 1 indexed citations
4.
Zhang, Zhen, Mengying Mao, Xiaoning Wang, et al.. (2025). Fusobacterium nucleatum ‐Derived Outer Membrane Vesicles Promote Immunotherapy Resistance via Changes in Tryptophan Metabolism in Tumour‐Associated Macrophages. Journal of Extracellular Vesicles. 14(4). e70070–e70070. 10 indexed citations
5.
Ouyang, Xiao, et al.. (2024). Nanocrystalline Ti‐Al‐Mo‐Zr‐Si Alloy (TC11) by Laser Powder Bed Fusion In‐situ Alloying. Advanced Engineering Materials. 26(10). 1 indexed citations
6.
Arimoto, Kei‐ichiro, Sayuri Miyauchi, Ty D. Troutman, et al.. (2023). Expansion of interferon inducible gene pool via USP18 inhibition promotes cancer cell pyroptosis. Nature Communications. 14(1). 251–251. 46 indexed citations
7.
Cui, Yuanbo, Ming Yan, Wei Wu, et al.. (2022). ESCCAL-1 promotes cell-cycle progression by interacting with and stabilizing galectin-1 in esophageal squamous cell carcinoma. npj Precision Oncology. 6(1). 12–12. 12 indexed citations
8.
Montauti, Elena, Samuel E. Weinberg, Peng Chu, et al.. (2022). A deubiquitination module essential for T reg fitness in the tumor microenvironment. Science Advances. 8(47). eabo4116–eabo4116. 35 indexed citations
9.
Lu, Tingwei, Zhen Zhang, Jianjun Zhang, et al.. (2022). CD73 in small extracellular vesicles derived from HNSCC defines tumour‐associated immunosuppression mediated by macrophages in the microenvironment. Journal of Extracellular Vesicles. 11(5). e12218–e12218. 61 indexed citations
10.
Lu, Tingwei, Zhen Zhang, Xinhua Pan, et al.. (2021). Caveolin‐1 promotes cancer progression via inhibiting ferroptosis in head and neck squamous cell carcinoma. Journal of Oral Pathology and Medicine. 51(1). 52–62. 49 indexed citations
11.
Johnson, D. T., Jiarong Zhou, Ashley V. Kroll, et al.. (2021). Acute myeloid leukemia cell membrane-coated nanoparticles for cancer vaccination immunotherapy. Leukemia. 36(4). 994–1005. 64 indexed citations
12.
Fan, Jun-Bao, Sayuri Miyauchi, Huizhong Xu, et al.. (2020). Type I Interferon Regulates a Coordinated Gene Network to Enhance Cytotoxic T Cell–Mediated Tumor Killing. Cancer Discovery. 10(3). 382–393. 42 indexed citations
13.
Stoner, Samuel A., Katherine Liu, Kei‐ichiro Arimoto, et al.. (2020). The RUNX1-ETO target gene RASSF2 suppresses t(8;21) AML development and regulates Rac GTPase signaling. Blood Cancer Journal. 10(2). 16–16. 9 indexed citations
14.
Stoner, Samuel A., Ming Yan, Katherine Liu, et al.. (2019). Hippo kinase loss contributes to del(20q) hematologic malignancies through chronic innate immune activation. Blood. 134(20). 1730–1744. 12 indexed citations
16.
Wang, Xu, Wei Cao, Jianjun Zhang, et al.. (2017). A covalently bound inhibitor triggers EZH 2 degradation through CHIP ‐mediated ubiquitination. The EMBO Journal. 36(9). 1243–1260. 74 indexed citations
17.
Huang, Yi-Jou, Ming Yan, Eun Hee Kim, et al.. (2017). RUNX1 Deficiency and SRSF2 Mutation Cooperate to Promote Myelodysplastic Syndrome Development. Blood. 130. 119–119. 1 indexed citations
18.
Arimoto, Kei‐ichiro, et al.. (2014). Plakophilin-2 Promotes Tumor Development by Enhancing Ligand-Dependent and -Independent Epidermal Growth Factor Receptor Dimerization and Activation. Molecular and Cellular Biology. 34(20). 3843–3854. 35 indexed citations
19.
Sun, Qiang, Ming Yan, Xiaojian Zhou, et al.. (2010). [Effects of Podoplanin on cell proliferation and cell cycle in oral leukoplakia cells].. PubMed. 45(1). 6–10. 1 indexed citations
20.
Zhang, Bin, et al.. (2007). A20 inhibits human salivary adenoid cystic carcinoma cells invasion via blocking nuclear factor-κB activation. Chinese Medical Journal. 120(20). 1830–1835. 11 indexed citations

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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