Ning Zhang

15.4k total citations · 4 hit papers
327 papers, 11.2k citations indexed

About

Ning Zhang is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Ning Zhang has authored 327 papers receiving a total of 11.2k indexed citations (citations by other indexed papers that have themselves been cited), including 157 papers in Molecular Biology, 66 papers in Immunology and 61 papers in Oncology. Recurrent topics in Ning Zhang's work include Nanoplatforms for cancer theranostics (37 papers), Nanoparticle-Based Drug Delivery (29 papers) and Advanced biosensing and bioanalysis techniques (25 papers). Ning Zhang is often cited by papers focused on Nanoplatforms for cancer theranostics (37 papers), Nanoparticle-Based Drug Delivery (29 papers) and Advanced biosensing and bioanalysis techniques (25 papers). Ning Zhang collaborates with scholars based in China, United States and Hong Kong. Ning Zhang's co-authors include Weishan Chen, Shengjie Xu, Ying Yin, Hua Guo, Joost J. Oppenheim, Yinsong Wang, Ping Zhou, Ruidong Xue, Kevin P. Francis and Ruirui Kong and has published in prestigious journals such as Nature, Journal of the American Chemical Society and Nucleic Acids Research.

In The Last Decade

Ning Zhang

313 papers receiving 11.1k citations

Hit Papers

5-Fluorouracil: Mechanisms of Resistance and Reversal Str... 2008 2026 2014 2020 2008 2022 2023 2024 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ning Zhang China 58 5.3k 2.2k 1.9k 1.9k 1.9k 327 11.2k
Yong Wang China 48 3.9k 0.7× 1.8k 0.8× 1.5k 0.8× 1.7k 0.9× 1.5k 0.8× 246 8.6k
Baorui Liu China 47 3.6k 0.7× 2.0k 0.9× 2.7k 1.4× 1.5k 0.8× 1.5k 0.8× 379 8.7k
Xiawei Wei China 56 5.9k 1.1× 1.8k 0.8× 2.8k 1.5× 1.5k 0.8× 3.1k 1.6× 190 12.9k
Qi Zhao China 52 4.3k 0.8× 2.8k 1.3× 3.1k 1.6× 1.2k 0.6× 2.4k 1.3× 373 12.4k
Lee Jia China 56 5.2k 1.0× 1.9k 0.9× 3.4k 1.7× 1.1k 0.6× 2.4k 1.3× 311 13.2k
Man Li China 56 3.8k 0.7× 2.2k 1.0× 1.9k 1.0× 1.0k 0.5× 1.8k 1.0× 354 9.8k
Mahmoud Reza Jaafari Iran 60 5.0k 1.0× 2.2k 1.0× 1.2k 0.6× 1.4k 0.7× 2.3k 1.2× 387 11.8k
Mahitosh Mandal India 64 6.0k 1.1× 1.7k 0.8× 2.5k 1.3× 1.5k 0.8× 812 0.4× 302 12.6k
Zhaohui Wang China 44 3.6k 0.7× 2.1k 1.0× 884 0.5× 1.2k 0.6× 1.6k 0.8× 202 7.7k
Min Wu China 59 7.4k 1.4× 3.4k 1.5× 1.6k 0.8× 2.1k 1.1× 1.4k 0.7× 294 14.8k

Countries citing papers authored by Ning Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Ning Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ning Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Ning Zhang. A scholar is included among the top collaborators of Ning 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 Ning Zhang. Ning 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
2.
Duan, Jin‐Ling, Ruixuan Wang, Xueyi Zheng, et al.. (2024). Deep learning model with pathological knowledge for detection of colorectal neuroendocrine tumor. Cell Reports Medicine. 5(10). 101785–101785. 3 indexed citations
3.
Zhang, Ning, Weihua Liu, Xuelian Hu, et al.. (2024). Novel Eu-dipeptide assemblies for a fluorescence sensing strategy to ultrasensitive determine trace sulfamethazine. Food Chemistry. 448. 139089–139089. 9 indexed citations
5.
Ma, Kun, Yufeng Liu, Yufeng Liu, et al.. (2024). Hepatocellular Carcinoma LINC01116 Outcompetes T Cells for Linoleic Acid and Accelerates Tumor Progression. Advanced Science. 11(21). e2400676–e2400676. 14 indexed citations
6.
Wang, Longrong, Longrong Wang, Ning Zhang, et al.. (2023). Safety and efficacy of GEMOX plus donafenib and tislelizumab as first‐line therapy for advanced epithelial malignant biliary tract cancer. Cancer Medicine. 12(11). 12263–12271. 4 indexed citations
7.
Sun, Li, et al.. (2023). LINC00869 Promotes Hepatocellular Carcinoma Metastasis via Protrusion Formation. Molecular Cancer Research. 22(3). 282–294. 1 indexed citations
8.
Du, Yaoyao, Junyu Shi, Ran Duan, et al.. (2022). cRGD peptide incorporated with patchouli alcohol loaded silk fibroin nanoparticles for enhanced targeting of inflammatory sites in colitis. Biomaterials Advances. 140. 213069–213069. 9 indexed citations
9.
Liang, Shuhang, Hongrui Guo, Kun Ma, et al.. (2021). A PLCB1–PI3K–AKT Signaling Axis Activates EMT to Promote Cholangiocarcinoma Progression. Cancer Research. 81(23). 5889–5903. 63 indexed citations
10.
Zeng, Judeng, Mengke Chen, Ning Zhang, et al.. (2018). IDDF2018-ABS-0140 The crosstalk of MTORC1 and DNA methylation in hepatocellular carcinoma. A24.1–A24. 2 indexed citations
11.
Liu, Yun, Xinran Zhang, Hao Zhuang, et al.. (2018). Demethylation-Induced Overexpression of Shc3 Drives c-Raf–Independent Activation of MEK/ERK in HCC. Cancer Research. 78(9). 2219–2232. 35 indexed citations
12.
Ao, Jian‐Yang, Xiao‐Dong Zhu, Zong‐Tao Chai, et al.. (2017). Colony-Stimulating Factor 1 Receptor Blockade Inhibits Tumor Growth by Altering the Polarization of Tumor-Associated Macrophages in Hepatocellular Carcinoma. Molecular Cancer Therapeutics. 16(8). 1544–1554. 153 indexed citations
13.
14.
Wang, Yan, Yuanyuan Liu, Yang Liu, et al.. (2015). A polymeric prodrug of cisplatin based on pullulan for the targeted therapy against hepatocellular carcinoma. International Journal of Pharmaceutics. 483(1-2). 89–100. 37 indexed citations
15.
Burnett, Joseph, Hasan Körkaya, Maria Ouzounova, et al.. (2015). Trastuzumab resistance induces EMT to transform HER2+ PTEN− to a triple negative breast cancer that requires unique treatment options. Scientific Reports. 5(1). 15821–15821. 54 indexed citations
16.
Wang, Yinsong, Yinsong Wang, Yang Liu, et al.. (2013). pH-sensitive pullulan-based nanoparticles for intracellular drug delivery. Polymer Chemistry. 5(2). 423–432. 40 indexed citations
17.
Liu, Jinlin, Ning Zhang, Qun Li, et al.. (2011). Tumor-Associated Macrophages Recruit CCR6+ Regulatory T Cells and Promote the Development of Colorectal Cancer via Enhancing CCL20 Production in Mice. PLoS ONE. 6(4). e19495–e19495. 208 indexed citations
18.
Zhang, Fei, Xiaofang Zhang, Menghui Li, et al.. (2010). mTOR Complex Component Rictor Interacts with PKCζ and Regulates Cancer Cell Metastasis. Cancer Research. 70(22). 9360–9370. 105 indexed citations
19.
Liu, Ying, Jingna Wang, Min Wu, et al.. (2009). Down-Regulation of 3-Phosphoinositide–Dependent Protein Kinase-1 Levels Inhibits Migration and Experimental Metastasis of Human Breast Cancer Cells. Molecular Cancer Research. 7(6). 944–954. 57 indexed citations
20.
Yang, De, Qian Chen, Shao Bo Su, et al.. (2008). Eosinophil-derived neurotoxin acts as an alarmin to activate the TLR2–MyD88 signal pathway in dendritic cells and enhances Th2 immune responses. The Journal of Experimental Medicine. 205(1). 79–90. 275 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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