Miaojing Wu

1.1k total citations
39 papers, 702 citations indexed

About

Miaojing Wu is a scholar working on Molecular Biology, Cancer Research and Neurology. According to data from OpenAlex, Miaojing Wu has authored 39 papers receiving a total of 702 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Molecular Biology, 15 papers in Cancer Research and 11 papers in Neurology. Recurrent topics in Miaojing Wu's work include RNA modifications and cancer (10 papers), Cancer-related molecular mechanisms research (10 papers) and Epigenetics and DNA Methylation (5 papers). Miaojing Wu is often cited by papers focused on RNA modifications and cancer (10 papers), Cancer-related molecular mechanisms research (10 papers) and Epigenetics and DNA Methylation (5 papers). Miaojing Wu collaborates with scholars based in China, Vietnam and Japan. Miaojing Wu's co-authors include Xingen Zhu, Shigang Lv, Minhua Ye, Yanghua Fan, Bing Xiao, Kai Huang, Lei Wu, Tengfeng Yan, Qing Hu and Wei Tu and has published in prestigious journals such as SHILAP Revista de lepidopterología, ACS Applied Materials & Interfaces and Journal of neurosurgery.

In The Last Decade

Miaojing Wu

38 papers receiving 698 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Miaojing Wu China 17 444 268 76 76 70 39 702
Shigang Lv China 15 396 0.9× 223 0.8× 83 1.1× 57 0.8× 69 1.0× 32 576
Marianna Trani Switzerland 8 527 1.2× 132 0.5× 61 0.8× 76 1.0× 70 1.0× 8 913
Qindong Guo China 12 565 1.3× 375 1.4× 88 1.2× 79 1.0× 49 0.7× 19 815
Meiqing Lou China 16 352 0.8× 142 0.5× 94 1.2× 82 1.1× 149 2.1× 40 705
Yanghua Fan China 18 286 0.6× 230 0.9× 168 2.2× 97 1.3× 63 0.9× 48 815
Agnieszka Zimna Poland 9 341 0.8× 186 0.7× 66 0.9× 57 0.8× 80 1.1× 24 709
Wanghao Chen China 15 574 1.3× 392 1.5× 58 0.8× 20 0.3× 41 0.6× 18 741
Zhongjie Yan China 16 346 0.8× 185 0.7× 211 2.8× 33 0.4× 23 0.3× 36 659
Kenta Ujifuku Japan 9 258 0.6× 227 0.8× 72 0.9× 54 0.7× 16 0.2× 23 544
Bo Han China 17 282 0.6× 135 0.5× 112 1.5× 121 1.6× 44 0.6× 62 880

Countries citing papers authored by Miaojing Wu

Since Specialization
Citations

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

Fields of papers citing papers by Miaojing Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Miaojing Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Miaojing Wu. A scholar is included among the top collaborators of Miaojing Wu 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 Miaojing Wu. Miaojing Wu 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.
Shu, Lei, Tengfeng Yan, Bing Xiao, et al.. (2025). Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment. Therapeutics and Clinical Risk Management. Volume 21. 293–307. 1 indexed citations
2.
Wu, Miaojing, et al.. (2024). Study on copper-to-copper bonding of three-dimensional integrated circuits using the quasicontinuum method. Physica Scripta. 99(6). 65114–65114. 5 indexed citations
3.
Shu, Lei, Hua Yan, Tengfeng Yan, et al.. (2024). Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage. Aging. 16(5). 4654–4669. 10 indexed citations
4.
Yan, Tengfeng, Ping Hu, Minhua Ye, et al.. (2024). ZNF384 transcriptionally activated MGST1 to confer TMZ resistance of glioma cells by negatively regulating ferroptosis. Cancer Chemotherapy and Pharmacology. 94(3). 323–336. 2 indexed citations
5.
Luo, Min, Shigang Lv, Wei Tu, et al.. (2024). Comprehensive analyses of m1A regulator-mediated modification patterns determining prognosis in lower-grade glioma (running title: m1A in LGG). Heliyon. 10(6). e27510–e27510. 1 indexed citations
6.
Hu, Ping, Tengfeng Yan, Lei Shu, et al.. (2024). Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis. International Journal of Surgery. 110(6). 3839–3847. 10 indexed citations
7.
8.
Huang, Kai, et al.. (2023). The HNF4A-CHPF pathway promotes proliferation and invasion through interactions with MAD1L1 in glioma. Aging. 15(20). 11052–11066. 2 indexed citations
9.
10.
Xiao, Bing, et al.. (2021). Circ_CLIP2 promotes glioma progression through targeting the miR-195-5p/HMGB3 axis. Journal of Neuro-Oncology. 154(2). 131–144. 15 indexed citations
11.
Wang, Peng, Miaojing Wu, Zewei Tu, et al.. (2020). Identification of RNA: 5-Methylcytosine Methyltransferases-Related Signature for Predicting Prognosis in Glioma. Frontiers in Oncology. 10. 1119–1119. 30 indexed citations
12.
Fan, Yanghua, Hua Min, Miaojing Wu, et al.. (2019). Preoperative Noninvasive Radiomics Approach Predicts Tumor Consistency in Patients With Acromegaly: Development and Multicenter Prospective Validation. Frontiers in Endocrinology. 10. 403–403. 43 indexed citations
13.
14.
Yan, Tengfeng, Miaojing Wu, Bing Xiao, et al.. (2018). Knockdown of HOXC6 inhibits glioma cell proliferation and induces cell cycle arrest by targeting WIF-1 in vitro and vivo. Pathology - Research and Practice. 214(11). 1818–1824. 14 indexed citations
15.
Wu, Miaojing, et al.. (2018). Involvement of oxidative stress in di-2-ethylhexyl phthalate (DEHP)-induced apoptosis of mouse NE-4C neural stem cells. NeuroToxicology. 70. 41–47. 38 indexed citations
16.
Cheng, Shiqi, Weiwei Gao, Xin Xu, et al.. (2017). Silymarin prevents NLRP3 inflammasome activation and protects against intracerebral hemorrhage. Biomedicine & Pharmacotherapy. 93. 308–315. 46 indexed citations
17.
Fan, Yanghua, et al.. (2017). Lentivirus-mediated knockdown of chondroitin polymerizing factor inhibits glioma cell growth in vitro. Oncology Reports. 38(2). 1149–1155. 22 indexed citations
18.
Cheng, Shiqi, Xin Xu, Weiwei Gao, et al.. (2016). Over-expression of LRIG1 suppresses biological function of pituitary adenoma via attenuation of PI3K/AKT and Ras/Raf/ERK pathways in vivo and in vitro. Journal of Huazhong University of Science and Technology [Medical Sciences]. 36(4). 558–563. 7 indexed citations
19.
Fan, Yanghua, et al.. (2016). BRAF-activated lncRNA predicts gastrointestinal cancer patient prognosis: a meta-analysis. Oncotarget. 8(4). 6295–6303. 21 indexed citations
20.
Xiao, Bing, Xuan Zhou, Minhua Ye, et al.. (2015). MicroRNA-566 modulates vascular endothelial growth factor by targeting Von Hippel-Landau in human glioblastoma in vitro and in vivo. Molecular Medicine Reports. 13(1). 379–385. 27 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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