Yong‐Qu Zhang

933 total citations
26 papers, 679 citations indexed

About

Yong‐Qu Zhang is a scholar working on Oncology, Molecular Biology and Cancer Research. According to data from OpenAlex, Yong‐Qu Zhang has authored 26 papers receiving a total of 679 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Oncology, 11 papers in Molecular Biology and 9 papers in Cancer Research. Recurrent topics in Yong‐Qu Zhang's work include Nanoplatforms for cancer theranostics (7 papers), Cancer Cells and Metastasis (5 papers) and Cancer-related molecular mechanisms research (5 papers). Yong‐Qu Zhang is often cited by papers focused on Nanoplatforms for cancer theranostics (7 papers), Cancer Cells and Metastasis (5 papers) and Cancer-related molecular mechanisms research (5 papers). Yong‐Qu Zhang collaborates with scholars based in China, Netherlands and Switzerland. Yong‐Qu Zhang's co-authors include Guo‐Jun Zhang, Xiaolong Wei, Wen‐He Huang, Ruiqin Yang, Kangliang Lou, Jing‐Wen Bai, Yuanke Liang, Peiyuan Wang, Haoyu Lin and Min Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Cancer Research.

In The Last Decade

Yong‐Qu Zhang

26 papers receiving 671 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yong‐Qu Zhang China 13 316 231 158 147 116 26 679
Xiaoya Zhao China 15 350 1.1× 298 1.3× 101 0.6× 160 1.1× 135 1.2× 33 763
Yirun Li China 17 309 1.0× 207 0.9× 80 0.5× 173 1.2× 171 1.5× 28 728
Hongjun Yuan China 15 220 0.7× 365 1.6× 141 0.9× 145 1.0× 171 1.5× 34 818
Elena Strekalova United States 13 486 1.5× 194 0.8× 148 0.9× 131 0.9× 133 1.1× 16 810
Meng-jun Qiu China 13 174 0.6× 188 0.8× 107 0.7× 65 0.4× 111 1.0× 28 535
Fangfang Han China 4 304 1.0× 256 1.1× 204 1.3× 286 1.9× 79 0.7× 6 817
Huaqing Shi China 6 320 1.0× 259 1.1× 228 1.4× 288 2.0× 80 0.7× 12 830
Bastien Doix Belgium 9 289 0.9× 193 0.8× 134 0.8× 249 1.7× 90 0.8× 10 666
Jinxiang Tan China 14 429 1.4× 129 0.6× 208 1.3× 253 1.7× 46 0.4× 21 818
Yan Du China 9 351 1.1× 258 1.1× 233 1.5× 309 2.1× 79 0.7× 22 873

Countries citing papers authored by Yong‐Qu Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Yong‐Qu Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yong‐Qu Zhang. 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 Yong‐Qu Zhang. The network helps show where Yong‐Qu Zhang may publish in the future.

Co-authorship network of co-authors of Yong‐Qu Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Yong‐Qu Zhang. A scholar is included among the top collaborators of Yong‐Qu Zhang 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 Yong‐Qu Zhang. Yong‐Qu Zhang 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.
2.
Zhang, Yong‐Qu, Yong‐Qu Zhang, Liang Song, et al.. (2024). Rare Earth Nanoprobes for Targeted Delineation of Triple Negative Breast Cancer and Enhancement of Radioimmunotherapy. Advanced Science. 11(29). e2309992–e2309992. 6 indexed citations
3.
Zhang, Yong‐Qu, Wanling Liu, Xiangjie Luo, et al.. (2023). Novel Self‐Assembled Multifunctional Nanoprobes for Second‐Near‐Infrared‐Fluorescence‐Image‐Guided Breast Cancer Surgery and Enhanced Radiotherapy Efficacy. Advanced Science. 10(10). e2205294–e2205294. 31 indexed citations
4.
5.
Wei, Min, Hong Chen, Zhong Luo, et al.. (2023). Methylation of ESR1 promoter induced by SNAI2–DNMT3B complex promotes epithelial–mesenchymal transition and correlates with poor prognosis in ERα‐positive breast cancers. SHILAP Revista de lepidopterología. 4(6). e403–e403. 7 indexed citations
6.
Zhang, Yong‐Qu, Xiangjie Luo, Liang Song, et al.. (2023). Novel Dual-Mode NIR-II/MRI Nanoprobe Targeting PD-L1 Accurately Evaluates the Efficacy of Immunotherapy for Triple-Negative Breast Cancer. International Journal of Nanomedicine. Volume 18. 5141–5157. 12 indexed citations
7.
Yang, Ruiqin, Peiyuan Wang, Kangliang Lou, et al.. (2022). Biodegradable Nanoprobe for NIR‐II Fluorescence Image‐Guided Surgery and Enhanced Breast Cancer Radiotherapy Efficacy. Advanced Science. 9(12). e2104728–e2104728. 68 indexed citations
8.
Liu, Wan‐Ling, Yong‐Qu Zhang, Liangtao Li, et al.. (2022). Application of molecular imaging in immune checkpoints therapy: From response assessment to prognosis prediction. Critical Reviews in Oncology/Hematology. 176. 103746–103746. 5 indexed citations
9.
Chen, Weiling, et al.. (2022). Integrative Analysis of the Expression Levels and Prognostic Values for NEK Family Members in Breast Cancer. Frontiers in Genetics. 13. 798170–798170. 9 indexed citations
10.
Zhang, Yong‐Qu, Yuanke Liang, Yang Wu, et al.. (2021). Notch3 inhibits cell proliferation and tumorigenesis and predicts better prognosis in breast cancer through transactivating PTEN. Cell Death and Disease. 12(6). 502–502. 34 indexed citations
11.
Zhang, Yong‐Qu, et al.. (2021). TRPC1 Inhibits Cell Proliferation/Invasion and Is Predictive of a Better Prognosis of Esophageal Squamous Cell Carcinoma. Frontiers in Oncology. 11. 627713–627713. 12 indexed citations
12.
Zhang, Yong‐Qu, et al.. (2021). Co-expression of VEGF-C and survivin predicts poor prognosis in esophageal squamous cell carcinoma. Translational Cancer Research. 10(1). 210–222. 6 indexed citations
13.
Zhang, Yong‐Qu, Fan Zhang, Min Chen, et al.. (2021). Mutant p53 and Twist1 Co-Expression Predicts Poor Prognosis and Is an Independent Prognostic Factor in Breast Cancer. Frontiers in Oncology. 11. 628814–628814. 1 indexed citations
14.
Zhang, Liying, et al.. (2020). TRPC1 inhibits the proliferation and migration of estrogen receptor-positive Breast cancer and gives a better prognosis by inhibiting the PI3K/AKT pathway. Breast Cancer Research and Treatment. 182(1). 21–33. 29 indexed citations
15.
Liang, Yuanke, Haoyu Lin, Xiaowei Dou, et al.. (2018). MiR-221/222 promote epithelial-mesenchymal transition by targeting Notch3 in breast cancer cell lines. npj Breast Cancer. 4(1). 20–20. 58 indexed citations
16.
Wu, Jundong, Chao‐Qun Hong, Wen-Jia Chen, et al.. (2017). L1 Cell Adhesion Molecule and Its Soluble Form sL1 Exhibit Poor Prognosis in Primary Breast Cancer Patients. Clinical Breast Cancer. 18(5). e851–e861. 13 indexed citations
17.
Dou, Xiaowei, Yuanke Liang, Haoyu Lin, et al.. (2017). Notch3 Maintains Luminal Phenotype and Suppresses Tumorigenesis and Metastasis of Breast Cancer via Trans-Activating Estrogen Receptor-α. Theranostics. 7(16). 4041–4056. 50 indexed citations
18.
Bai, Jing‐Wen, Yong‐Qu Zhang, Yaochen Li, & Guo‐Jun Zhang. (2017). Analysis of Epithelial–Mesenchymal Transition Induced by Overexpression of Twist. Methods in molecular biology. 1652. 259–274. 7 indexed citations
19.
20.
Zhang, Yong‐Qu, Xiaolong Wei, Yuanke Liang, et al.. (2015). Over-Expressed Twist Associates with Markers of Epithelial Mesenchymal Transition and Predicts Poor Prognosis in Breast Cancers via ERK and Akt Activation. PLoS ONE. 10(8). e0135851–e0135851. 66 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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