Ming Guo

12.7k total citations · 3 hit papers
26 papers, 3.1k citations indexed

About

Ming Guo is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Ming Guo has authored 26 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 7 papers in Immunology and 5 papers in Oncology. Recurrent topics in Ming Guo's work include Parkinson's Disease Mechanisms and Treatments (4 papers), T-cell and B-cell Immunology (4 papers) and Drug Transport and Resistance Mechanisms (3 papers). Ming Guo is often cited by papers focused on Parkinson's Disease Mechanisms and Treatments (4 papers), T-cell and B-cell Immunology (4 papers) and Drug Transport and Resistance Mechanisms (3 papers). Ming Guo collaborates with scholars based in United States, China and Netherlands. Ming Guo's co-authors include Ralph M. Steinman, Karsten Mahnke, Michel C. Nussenzweig, Jeffrey V. Ravetch, Daniel Hawiger, Kayo Inaba, Miguel N. Rivera, Yair Dorsett, Yongheng Chen and Hudie Wei and has published in prestigious journals such as Science, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Ming Guo

23 papers receiving 3.1k citations

Hit Papers

Dendritic Cells Induce Peripheral T Cell Unresponsiveness... 2000 2026 2008 2017 2001 2023 2000 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ming Guo United States 14 2.0k 1.1k 491 210 187 26 3.1k
Sonja C. Stadler Germany 19 1.2k 0.6× 1.4k 1.3× 283 0.6× 237 1.1× 338 1.8× 27 2.9k
Jorge L. Martínez‐Torrecuadrada Spain 32 675 0.3× 1.2k 1.1× 467 1.0× 266 1.3× 191 1.0× 62 2.7k
G. Herma Renkema Netherlands 28 1.0k 0.5× 2.9k 2.6× 322 0.7× 443 2.1× 102 0.5× 46 4.0k
Munehiro Nakata Japan 26 1.5k 0.7× 1.1k 1.0× 233 0.5× 267 1.3× 101 0.5× 83 2.7k
Antoine Ménoret United States 26 1.4k 0.7× 1.6k 1.5× 297 0.6× 266 1.3× 116 0.6× 68 2.6k
Xiuping Fu China 18 781 0.4× 834 0.8× 1.1k 2.1× 104 0.5× 231 1.2× 49 2.3k
Jean‐Pierre Szikora Belgium 16 2.2k 1.1× 1.6k 1.4× 879 1.8× 334 1.6× 273 1.5× 22 3.1k
Nora Bijl Netherlands 15 606 0.3× 925 0.8× 233 0.5× 241 1.1× 85 0.5× 17 1.8k
Olivier Donzé Switzerland 25 979 0.5× 2.3k 2.0× 308 0.6× 289 1.4× 352 1.9× 35 3.5k
Tencho Tenev United Kingdom 31 1.5k 0.8× 3.5k 3.2× 837 1.7× 580 2.8× 169 0.9× 46 4.4k

Countries citing papers authored by Ming Guo

Since Specialization
Citations

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

Fields of papers citing papers by Ming Guo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Guo

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Guo. A scholar is included among the top collaborators of Ming Guo 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 Ming Guo. Ming Guo 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.
Lin, Hang, Lingzhi Qu, Hudie Wei, et al.. (2025). Characterization of Bozitinib as a potential therapeutic agent for MET-amplified gastric cancer. Communications Biology. 8(1). 134–134.
3.
Deng, Yalan, Liqing Lu, Dandan Zhu, et al.. (2024). MafG/MYH9-LCN2 axis promotes liver fibrosis through inhibiting ferroptosis of hepatic stellate cells. Cell Death and Differentiation. 31(9). 1127–1139. 20 indexed citations
4.
Jiang, Longying, Xujun Liang, Shuyan Dai, et al.. (2024). Structural characterization of the DNA binding mechanism of retinoic acid-related orphan receptor gamma. Structure. 32(4). 467–475.e3. 2 indexed citations
5.
Hu, Ke, Fangyuan Zhao, Faqin Dong, et al.. (2024). Efficient three-dimensional electrochemical degradation of alizarin red by CeO2−MnO2/NF particle electrode synergized with ozone. Journal of Environmental Sciences. 154. 365–377. 3 indexed citations
6.
Qu, Lingzhi, Hang Lin, Shuyan Dai, et al.. (2023). Structural insight into the macrocyclic inhibitor TPX-0022 of c-Met and c-Src. Computational and Structural Biotechnology Journal. 21. 5712–5718. 5 indexed citations
7.
Jiang, Longying, Xujun Liang, Shuyan Dai, et al.. (2023). Structural basis of the farnesoid X receptor/retinoid X receptor heterodimer on inverted repeat DNA. Computational and Structural Biotechnology Journal. 21. 3149–3157. 12 indexed citations
8.
Wang, Haolan, Ming Guo, Hudie Wei, & Yongheng Chen. (2023). Targeting p53 pathways: mechanisms, structures and advances in therapy. Signal Transduction and Targeted Therapy. 8(1). 92–92. 486 indexed citations breakdown →
9.
Jiang, Longying, Hudie Wei, Shuyan Dai, et al.. (2022). Structural insight into the molecular mechanism of cilofexor binding to the farnesoid X receptor. Biochemical and Biophysical Research Communications. 595. 1–6. 5 indexed citations
10.
Dai, Shuyan, Lingzhi Qu, Jun Li, et al.. (2022). Structural insight into the ligand binding mechanism of aryl hydrocarbon receptor. Nature Communications. 13(1). 6234–6234. 37 indexed citations
11.
Guo, Ming, Shuyan Dai, ­Jun Li­, et al.. (2022). Structural study of ponatinib in inhibiting SRC kinase. Biochemical and Biophysical Research Communications. 598. 15–19. 10 indexed citations
12.
Wei, Hudie, Lingzhi Qu, Shuyan Dai, et al.. (2021). Structural insight into the molecular mechanism of p53-mediated mitochondrial apoptosis. Nature Communications. 12(1). 2280–2280. 76 indexed citations
13.
Jiang, Longying, Desheng Xiao, Yubin Li, et al.. (2020). Structural basis of tropifexor as a potent and selective agonist of farnesoid X receptor. Biochemical and Biophysical Research Communications. 534. 1047–1052. 18 indexed citations
14.
Guo, Ming, Shuyan Dai, Daichao Wu, et al.. (2020). Characterization of ibrutinib as a non-covalent inhibitor of SRC-family kinases. Bioorganic & Medicinal Chemistry Letters. 34. 127757–127757. 10 indexed citations
15.
Ma, Peng, Jina Yun, Hansong Deng, & Ming Guo. (2018). Atg1-mediated autophagy suppresses tissue degeneration inpink1/parkinmutants by promoting mitochondrial fission inDrosophila. Molecular Biology of the Cell. 29(26). 3082–3092. 24 indexed citations
16.
Wu, Daichao, Lingzhi Qu, Yang Fu, et al.. (2016). Expression and purification of the kinase domain of PINK1 in Pichia pastoris. Protein Expression and Purification. 128. 67–72. 4 indexed citations
17.
Hartenstein, Volker, et al.. (2016). Developmental analysis of the dopamine‐containing neurons of the Drosophila brain. The Journal of Comparative Neurology. 525(2). 363–379. 31 indexed citations
18.
Niedbala, R. Sam, et al.. (2007). Evaluation of UPlink–RSV. Annals of the New York Academy of Sciences. 1098(1). 476–485. 41 indexed citations
20.
Guo, Ming, Ziva Misulovin, Maggi Pack, et al.. (2000). A monoclonal antibody to the DEC-205 endocytosis receptor on human dendritic cells. Human Immunology. 61(8). 729–738. 106 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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