Qingkai Yang

2.8k total citations
37 papers, 1.7k citations indexed

About

Qingkai Yang is a scholar working on Molecular Biology, Cancer Research and Cell Biology. According to data from OpenAlex, Qingkai Yang has authored 37 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 7 papers in Cancer Research and 6 papers in Cell Biology. Recurrent topics in Qingkai Yang's work include RNA modifications and cancer (7 papers), interferon and immune responses (6 papers) and Melanoma and MAPK Pathways (6 papers). Qingkai Yang is often cited by papers focused on RNA modifications and cancer (7 papers), interferon and immune responses (6 papers) and Melanoma and MAPK Pathways (6 papers). Qingkai Yang collaborates with scholars based in China, United States and United Kingdom. Qingkai Yang's co-authors include Jiing‐Dwan Lee, Nathanael S. Gray, Xianming Deng, Matthew P. Patricelli, John R. Yates, Tyzoon Nomanbhoy, Colleen Fearns, Bingwen Lu, Dario R. Alessi and Nicolas Dzamko and has published in prestigious journals such as Cell, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Qingkai Yang

34 papers receiving 1.7k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Qingkai Yang China 21 1.3k 310 268 232 227 37 1.7k
Jiing‐Dwan Lee United States 21 2.1k 1.6× 472 1.5× 286 1.1× 450 1.9× 303 1.3× 26 2.7k
Emmanuel Normant United States 22 1.3k 1.0× 370 1.2× 111 0.4× 417 1.8× 115 0.5× 50 2.0k
Alastair J. King United States 18 1.6k 1.2× 659 2.1× 103 0.4× 289 1.2× 149 0.7× 28 2.0k
Katrina Diener United States 13 1.3k 1.0× 237 0.8× 85 0.3× 199 0.9× 231 1.0× 18 1.7k
Sarah Hanrahan United Kingdom 17 1.2k 0.9× 234 0.8× 90 0.3× 337 1.5× 370 1.6× 24 1.8k
Richard I. Feldman United States 11 1.3k 1.0× 524 1.7× 66 0.2× 347 1.5× 209 0.9× 15 1.8k
Joseph Negri United States 21 933 0.7× 613 2.0× 85 0.3× 164 0.7× 219 1.0× 47 1.6k
Graham C. Fletcher Canada 18 1.4k 1.1× 458 1.5× 290 1.1× 684 2.9× 242 1.1× 30 2.3k
Darrin D. Stuart United States 25 1.7k 1.3× 819 2.6× 71 0.3× 185 0.8× 241 1.1× 50 2.3k
Elena Ardini Italy 21 1.0k 0.8× 537 1.7× 83 0.3× 113 0.5× 160 0.7× 38 1.6k

Countries citing papers authored by Qingkai Yang

Since Specialization
Citations

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

Fields of papers citing papers by Qingkai Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qingkai Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Qingkai Yang. A scholar is included among the top collaborators of Qingkai Yang 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 Qingkai Yang. Qingkai Yang 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.
Chen, Dan, Liang Zhou, Chaoqun Chen, et al.. (2024). RBMS1 Coordinates with the m 6 A Reader YTHDF1 to Promote NSCLC Metastasis through Stimulating S100P Translation. Advanced Science. 11(15). e2307122–e2307122. 11 indexed citations
2.
Gao, Xiaoqian, et al.. (2024). Cyclic GMP-AMP synthase recognizes the physical features of DNA. Acta Pharmacologica Sinica. 46(2). 264–270. 3 indexed citations
3.
Wang, Lina, Kai Wang, Na Wang, et al.. (2023). Spermine enhances antiviral and anticancer responses by stabilizing DNA binding with the DNA sensor cGAS. Immunity. 56(2). 272–288.e7. 26 indexed citations
4.
Wang, Lina, Kai Wang, Na Wang, et al.. (2022). DNA mechanical flexibility controls DNA potential to activate cGAS-mediated immune surveillance. Nature Communications. 13(1). 7107–7107. 20 indexed citations
5.
Duan, Jiang‐Jie, Di Wang, Junjie Chen, et al.. (2022). An aldehyde dehydrogenase 1A3 inhibitor attenuates the metastasis of human colorectal cancer. Cancer Letters. 536. 215662–215662. 22 indexed citations
6.
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8.
Wu, Yueguang, Yingqiu Zhang, Duchuang Wang, et al.. (2020). USP29 enhances chemotherapy-induced stemness in non-small cell lung cancer via stabilizing Snail1 in response to oxidative stress. Cell Death and Disease. 11(9). 796–796. 30 indexed citations
9.
Li, Songyu, Yixiang Zhang, Na Wang, et al.. (2020). Pan-cancer analysis reveals synergistic effects of CDK4/6i and PARPi combination treatment in RB-proficient and RB-deficient breast cancer cells. Cell Death and Disease. 11(4). 219–219. 20 indexed citations
10.
Song, Chengli, Lina Wang, Xiaoyan Wu, et al.. (2018). PML Recruits TET2 to Regulate DNA Modification and Cell Proliferation in Response to Chemotherapeutic Agent. Cancer Research. 78(10). 2475–2489. 28 indexed citations
11.
Song, Chengli, Lina Wang, Qiang Xu, et al.. (2017). Targeting BMK1 Impairs the Drug Resistance to Combined Inhibition of BRAF and MEK1/2 in Melanoma. Scientific Reports. 7(1). 46244–46244. 22 indexed citations
12.
Zhu, Yuting, Guangyu Zhou, Qiang Xu, et al.. (2017). LC-MS-MS quantitative analysis reveals the association between FTO and DNA methylation. PLoS ONE. 12(4). e0175849–e0175849. 23 indexed citations
13.
Chen, Runqiang, Qingkai Yang, & Jiing‐Dwan Lee. (2012). BMK1 Kinase Suppresses Epithelial–Mesenchymal Transition through the Akt/GSK3β Signaling Pathway. Cancer Research. 72(6). 1579–1587. 44 indexed citations
14.
Yang, Qingkai, et al.. (2012). BMK1 is involved in the regulation of p53 through disrupting the PML–MDM2 interaction. Oncogene. 32(26). 3156–3164. 33 indexed citations
15.
Patricelli, Matthew P., Tyzoon Nomanbhoy, Jiangyue Wu, et al.. (2011). In Situ Kinase Profiling Reveals Functionally Relevant Properties of Native Kinases. Chemistry & Biology. 18(6). 699–710. 259 indexed citations
16.
Deng, Xianming, Nicolas Dzamko, Alan R. Prescott, et al.. (2011). Characterization of a selective inhibitor of the Parkinson's disease kinase LRRK2. Nature Chemical Biology. 7(4). 203–205. 340 indexed citations
17.
Yang, Qingkai, Xianming Deng, Bingwen Lu, et al.. (2010). Pharmacological Inhibition of BMK1 Suppresses Tumor Growth through Promyelocytic Leukemia Protein. Cancer Cell. 18(3). 258–267. 154 indexed citations
18.
Chen, Yanling, Bingwen Lu, Qingkai Yang, et al.. (2009). Combined Integrin Phosphoproteomic Analyses and Small Interfering RNA–Based Functional Screening Identify Key Regulators for Cancer Cell Adhesion and Migration. Cancer Research. 69(8). 3713–3720. 88 indexed citations
19.
Yang, Qingkai, Bingwen Lu, Wei Yi, et al.. (2009). CDC25B Mediates Rapamycin-Induced Oncogenic Responses in Cancer Cells. Cancer Research. 69(6). 2663–2668. 99 indexed citations
20.
Yang, Qingkai, Yanling Chen, Vasco Oliveira, et al.. (2009). Kinome Sirna Screen Identifies SMG-1 as a Negative Regulator of Hypoxia-inducible Factor-1α in Hypoxia. Journal of Biological Chemistry. 284(25). 16752–16758. 20 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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