Wubing Zhang

3.4k total citations · 1 hit paper
22 papers, 1.4k citations indexed

About

Wubing Zhang is a scholar working on Molecular Biology, Oncology and Immunology. According to data from OpenAlex, Wubing Zhang has authored 22 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 9 papers in Oncology and 7 papers in Immunology. Recurrent topics in Wubing Zhang's work include Cancer Immunotherapy and Biomarkers (6 papers), Single-cell and spatial transcriptomics (5 papers) and Immune cells in cancer (5 papers). Wubing Zhang is often cited by papers focused on Cancer Immunotherapy and Biomarkers (6 papers), Single-cell and spatial transcriptomics (5 papers) and Immune cells in cancer (5 papers). Wubing Zhang collaborates with scholars based in China, United States and Norway. Wubing Zhang's co-authors include X. Shirley Liu, Jingxin Fu, Peng Jiang, Karen Li, Jing Zhang, Changxin Wan, Myles Brown, Xiaoqing Wang, Ziyi Li and Binbin Wang and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Wubing Zhang

21 papers receiving 1.4k citations

Hit Papers

Large-scale public data reuse to model immunotherapy resp... 2020 2026 2022 2024 2020 200 400 600
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. Large-scale public data reuse to model immunotherapy response and resistance
    2020 697 citations Jingxin Fu, Karen Li et al. Genome Medicine profile →

Peers

Wubing Zhang
Jeonghee Cho South Korea
Ayse Bassez Belgium
David Balli United States
Nnennaya Kanu United Kingdom
Toni M. Brand United States
Alberto Fusi Germany
Mirjam C. Boelens Netherlands
Xintao Qiu United States
Siddhartha Deb Australia
Sasha E. Stanton United States
Jeonghee Cho South Korea
Wubing Zhang
Citations per year, relative to Wubing Zhang Wubing Zhang (= 1×) peers Jeonghee Cho

Countries citing papers authored by Wubing Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Wubing Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wubing Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Wubing Zhang. A scholar is included among the top collaborators of Wubing 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 Wubing Zhang. Wubing 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.
Kang, Minji, Gunsagar S. Gulati, José Juan Almagro Armenteros, et al.. (2025). Improved reconstruction of single-cell developmental potential with CytoTRACE 2. Nature Methods. 22(11). 2258–2263. 2 indexed citations
2.
Dong, Shuyun, Hongbo Jiang, Wubing Zhang, et al.. (2025). Self-assembled ferritin tuberculosis nanovaccines targeting ESAT-6 and CFP-10 elicit potent immunogenicity in mice. International Journal of Biological Macromolecules. 320(Pt 4). 146101–146101.
3.
4.
5.
Earland, Noah, Wubing Zhang, Abul Usmani, et al.. (2023). CD4 T cells and toxicity from immune checkpoint blockade. Immunological Reviews. 318(1). 96–109. 9 indexed citations
6.
Ito, Yoshinaga, Deng Pan, Wubing Zhang, et al.. (2023). Addressing Tumor Heterogeneity by Sensitizing Resistant Cancer Cells to T cell–Secreted Cytokines. Cancer Discovery. 13(5). 1186–1209. 13 indexed citations
7.
Wang, Xujun, Sailing Shi, Wubing Zhang, et al.. (2023). Cancer Cell Resistance to IFNγ Can Occur via Enhanced Double-Strand Break Repair Pathway Activity. Cancer Immunology Research. 11(3). 381–398. 6 indexed citations
8.
Zhang, Yi, Guanjue Xiang, Zexian Zeng, et al.. (2023). MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment. Nature Communications. 14(1). 2634–2634. 11 indexed citations
9.
Vahid, Milad R., Chloé B. Steen, Wubing Zhang, et al.. (2023). High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE. Nature Biotechnology. 41(11). 1543–1548. 80 indexed citations
10.
Zeng, Zexian, Shengqing Gu, Lin Yang, et al.. (2022). Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response. Science Advances. 8(41). eabm8564–eabm8564. 17 indexed citations
11.
Zeng, Zexian, Shengqing Gu, Lin Yang, et al.. (2022). Hippo Signaling Pathway Regulates Cancer Cell–Intrinsic MHC-II Expression. Cancer Immunology Research. 10(12). 1559–1569. 14 indexed citations
12.
Zhang, Wubing, Shourya S. Roy Burman, Jiaye Chen, et al.. (2022). Machine Learning Modeling of Protein-Intrinsic Features Predicts Tractability of Targeted Protein Degradation. Genomics Proteomics & Bioinformatics. 20(5). 882–898. 26 indexed citations
13.
Tokheim, Collin, Xiaoqing Wang, Richard T. Timms, et al.. (2021). Systematic characterization of mutations altering protein degradation in human cancers. Molecular Cell. 81(6). 1292–1308.e11. 52 indexed citations
14.
Zeng, Zexian, Lin Yang, Dian Li, et al.. (2021). TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response. Nucleic Acids Research. 50(D1). D1391–D1397. 73 indexed citations
15.
Shi, Sailing, Shengqing Gu, Wubing Zhang, et al.. (2020). Inhibition of MAN2A1 Enhances the Immune Response to Anti–PD-L1 in Human Tumors. Clinical Cancer Research. 26(22). 5990–6002. 45 indexed citations
16.
Leng, Xiaohong, Shuren Wang, Jiawei Song, et al.. (2020). LINC01272 Promotes Migration and Invasion of Gastric Cancer Cells via EMT. SHILAP Revista de lepidopterología. 1 indexed citations
17.
Fu, Jingxin, Karen Li, Wubing Zhang, et al.. (2020). Large-scale public data reuse to model immunotherapy response and resistance. Genome Medicine. 12(1). 21–21. 697 indexed citations breakdown →
18.
Liu, Xiaojing, Tingting Liu, Yafang Shang, et al.. (2020). ERCC6L2 promotes DNA orientation-specific recombination in mammalian cells. Cell Research. 30(9). 732–744. 43 indexed citations
19.
Wang, Binbin, Mei Wang, Wubing Zhang, et al.. (2019). Integrative analysis of pooled CRISPR genetic screens using MAGeCKFlute. Nature Protocols. 14(3). 756–780. 236 indexed citations
20.
Wu, Lingxiang, Xiujie Chen, Denan Zhang, et al.. (2016). IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering. PLoS ONE. 11(10). e0164542–e0164542. 4 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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