Xingda Zhang

949 total citations · 1 hit paper
21 papers, 613 citations indexed

About

Xingda Zhang is a scholar working on Molecular Biology, Cancer Research and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Xingda Zhang has authored 21 papers receiving a total of 613 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 10 papers in Cancer Research and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Xingda Zhang's work include RNA modifications and cancer (9 papers), Cancer-related molecular mechanisms research (7 papers) and Epigenetics and DNA Methylation (6 papers). Xingda Zhang is often cited by papers focused on RNA modifications and cancer (9 papers), Cancer-related molecular mechanisms research (7 papers) and Epigenetics and DNA Methylation (6 papers). Xingda Zhang collaborates with scholars based in China and Serbia. Xingda Zhang's co-authors include Shouping Xu, Da Pang, Huajiang Chen, Bo Hu, Jianxi Wang, Fazhi Zang, Qin Wang, Shipeng Ning, Kun Qiao and Lin Wan and has published in prestigious journals such as Chemical Engineering Journal, Desalination and Molecular Cancer.

In The Last Decade

Xingda Zhang

18 papers receiving 606 citations

Hit Papers

Metabolic reprogramming and therapeutic resistance in pri... 2024 2026 2025 2024 10 20 30 40 50
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Metabolic reprogramming and therapeutic resistance in primary and metastatic breast cancer
    2024 55 citations Shan Liu, Xingda Zhang et al. Molecular Cancer profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xingda Zhang China 11 450 301 75 57 52 21 613
Xingang Cui China 11 273 0.6× 217 0.7× 112 1.5× 122 2.1× 71 1.4× 19 552
Xuelin Sun China 8 338 0.8× 273 0.9× 36 0.5× 32 0.6× 35 0.7× 12 550
Yanyan Wang China 11 438 1.0× 205 0.7× 29 0.4× 69 1.2× 37 0.7× 32 576
Yefei Huang China 15 285 0.6× 117 0.4× 70 0.9× 51 0.9× 23 0.4× 31 563
Huixia Cao China 13 226 0.5× 134 0.4× 38 0.5× 59 1.0× 20 0.4× 40 471
Cosmin Lisencu Romania 9 219 0.5× 180 0.6× 46 0.6× 38 0.7× 27 0.5× 25 466

Countries citing papers authored by Xingda Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Xingda Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xingda Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Xingda Zhang. A scholar is included among the top collaborators of Xingda 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 Xingda Zhang. Xingda 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.
Ruan, Ye Chun, Xing‐Yuan Liu, Yun Jin, et al.. (2025). Personalized predictions of neoadjuvant chemotherapy response in breast cancer using machine learning and full-field digital mammography radiomics. Frontiers in Medicine. 12. 1582560–1582560.
2.
Qiao, Kun, Mingzhu Chen, Meng Lyu, et al.. (2024). Sonocatalysis-responsive nanodrug platforms for radioimmunotherapy against breast cancer. Chemical Engineering Journal. 503. 158394–158394. 1 indexed citations
3.
Wang, Siyu, Xingda Zhang, Hao Wu, et al.. (2024). FTO activates PD-L1 promotes immunosuppression in breast cancer via the m6A/YTHDF3/PDK1 axis under hypoxic conditions. Journal of Advanced Research. 76. 191–206. 15 indexed citations
4.
Liu, Shan, Xingda Zhang, Wenzheng Wang, et al.. (2024). Metabolic reprogramming and therapeutic resistance in primary and metastatic breast cancer. Molecular Cancer. 23(1). 261–261. 55 indexed citations breakdown →
5.
Zhang, Xingda, et al.. (2023). Precise subtyping reveals immune heterogeneity for hormone receptor-positive breast cancer. Computers in Biology and Medicine. 163. 107222–107222.
6.
Li, Wei, Yi Hao, Xingda Zhang, Shouping Xu, & Da Pang. (2022). Targeting RNA N6-methyladenosine modification: a precise weapon in overcoming tumor immune escape. Molecular Cancer. 21(1). 176–176. 31 indexed citations
7.
8.
Zhang, Xin, Xingda Zhang, Guozheng Li, et al.. (2022). A Novel Necroptosis-Associated lncRNA Signature Can Impact the Immune Status and Predict the Outcome of Breast Cancer. Journal of Immunology Research. 2022. 1–21. 14 indexed citations
9.
Zhang, Xingda, Jiaqi Zeng, Jianyu Wang, et al.. (2022). Revealing the Potential Markers of N(4)-Acetylcytidine through acRIP-seq in Triple-Negative Breast Cancer. Genes. 13(12). 2400–2400. 18 indexed citations
10.
Wang, Yihan, Yan Zhang, Yan Huang, et al.. (2021). Intratumor heterogeneity of breast cancer detected by epialleles shows association with hypoxic microenvironment. Theranostics. 11(9). 4403–4420. 9 indexed citations
11.
Lv, Wenhua, et al.. (2021). Exploring effects of DNA methylation and gene expression on pan-cancer drug response by mathematical models. Experimental Biology and Medicine. 246(14). 1626–1642. 1 indexed citations
12.
Huang, Yan, Dianshuang Zhou, Yihan Wang, et al.. (2020). Prediction of Transcription Factors Binding Events Based on Epigenetic Modifications in Different Human Cells. Epigenomics. 12(16). 1443–1456. 25 indexed citations
13.
Qiao, Kun, Shipeng Ning, Lin Wan, et al.. (2019). LINC00673 is activated by YY1 and promotes the proliferation of breast cancer cells via the miR-515-5p/MARK4/Hippo signaling pathway. Journal of Experimental & Clinical Cancer Research. 38(1). 418–418. 127 indexed citations
14.
15.
Gu, Yue, Xingda Zhang, Mu Su, et al.. (2019). Prediction of circRNAs Based on the DNA Methylation-Mediated Feature Sponge Function in Breast Cancer. Frontiers in Bioengineering and Biotechnology. 7. 365–365. 9 indexed citations
16.
Wang, Qin, et al.. (2019). Landscape of tumor suppressor long noncoding RNAs in breast cancer. Journal of Experimental & Clinical Cancer Research. 38(1). 79–79. 52 indexed citations
17.
Zhang, Jian, Shiyao Sui, Hao Wu, et al.. (2019). The transcriptional landscape of lncRNAs reveals the oncogenic function of LINC00511 in ER-negative breast cancer. Cell Death and Disease. 10(8). 48 indexed citations
18.
Zhu, Jiang, Mu Su, Xingda Zhang, et al.. (2019). Development of a Method for Identifying and Functionally Analyzing Allele-Specific DNA Methylation Based on BS-Seq Data. Epigenomics. 11(15). 1679–1692. 1 indexed citations
19.
Chen, Jiayan, et al.. (1981). Morphology of aromatic polyamide type asymmetric reverse osmosis membranes.. MEMBRANE. 6(1). 45–57.
20.
Chen, Jiayan, et al.. (1980). Morphology of aromatic polyamide type asymmetric reverse osmosis membranes. Desalination. 34(1-2). 97–112. 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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