Ming Shi

1.5k total citations
51 papers, 1.1k citations indexed

About

Ming Shi is a scholar working on Molecular Biology, Immunology and Epidemiology. According to data from OpenAlex, Ming Shi has authored 51 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 18 papers in Immunology and 11 papers in Epidemiology. Recurrent topics in Ming Shi's work include RNA Research and Splicing (7 papers), Immune Response and Inflammation (6 papers) and interferon and immune responses (5 papers). Ming Shi is often cited by papers focused on RNA Research and Splicing (7 papers), Immune Response and Inflammation (6 papers) and interferon and immune responses (5 papers). Ming Shi collaborates with scholars based in China, United States and United Kingdom. Ming Shi's co-authors include Henry Jay Forman, Xi Chen, Kai Li, Qiong Wu, Yuanfei Yao, Shuai Qu, Henry A. Choy, Evelyne Gozal, Hao Wu and Setu M. Vora and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Ming Shi

49 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ming Shi China 17 543 343 243 183 89 51 1.1k
Gernot Kriegshäuser Austria 17 358 0.7× 316 0.9× 105 0.4× 213 1.2× 46 0.5× 53 1.1k
Patricia Lagadec France 18 417 0.8× 235 0.7× 139 0.6× 161 0.9× 51 0.6× 46 1.0k
Magdalena Klink Poland 18 456 0.8× 456 1.3× 166 0.7× 244 1.3× 77 0.9× 73 1.2k
Maria Rosaria Ruocco Italy 25 886 1.6× 472 1.4× 330 1.4× 515 2.8× 106 1.2× 51 1.8k
Nannan Zhou China 13 597 1.1× 379 1.1× 114 0.5× 267 1.5× 57 0.6× 39 1.1k
Aline Sandouk United States 9 360 0.7× 576 1.7× 146 0.6× 118 0.6× 59 0.7× 14 1.2k
Irene Martínez‐Martínez Spain 21 479 0.9× 108 0.3× 202 0.8× 76 0.4× 34 0.4× 60 1.1k
Ziqi Zhang China 12 484 0.9× 285 0.8× 98 0.4× 167 0.9× 127 1.4× 26 958
Wenwen Wang China 13 694 1.3× 451 1.3× 193 0.8× 89 0.5× 83 0.9× 29 1.1k
Mutian Jia China 13 615 1.1× 447 1.3× 149 0.6× 89 0.5× 115 1.3× 21 937

Countries citing papers authored by Ming Shi

Since Specialization
Citations

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

Fields of papers citing papers by Ming Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Shi. A scholar is included among the top collaborators of Ming Shi 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 Shi. Ming Shi 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.
Fontana, Pietro, Gang Du, Haiwei Zhang, et al.. (2024). Small-molecule GSDMD agonism in tumors stimulates antitumor immunity without toxicity. Cell. 187(22). 6165–6181.e22. 49 indexed citations
2.
Vora, Setu M., Pietro Fontana, Tianyang Mao, et al.. (2022). Targeting stem-loop 1 of the SARS-CoV-2 5′ UTR to suppress viral translation and Nsp1 evasion. Proceedings of the National Academy of Sciences. 119(9). 70 indexed citations
3.
Xia, Peng, Xudong Xing, Cuixian Yang, et al.. (2022). Activation-induced pyroptosis contributes to the loss of MAIT cells in chronic HIV-1 infected patients. Military Medical Research. 9(1). 24–24. 29 indexed citations
4.
Chen, Xi, Hongjin Wu, Jia Feng, et al.. (2021). Transcriptome profiling unveils GAP43 regulates ABC transporters and EIF2 signaling in colorectal cancer cells. BMC Cancer. 21(1). 24–24. 11 indexed citations
5.
Yang, Shuang, Ming Zhang, Li Yun, et al.. (2020). THE EFFECT OF MUSK EXTRACTION PROCEDURES ON THE STRESS RESPONSE OF FARMED FOREST MUSK DEER (MOSCHUS BEREZOVSKII). The Journal of Animal and Plant Sciences. 30(6). 1 indexed citations
6.
Shi, Ming, Pengfei Zhang, Setu M. Vora, & Hao Wu. (2020). Higher-order assemblies in innate immune and inflammatory signaling: A general principle in cell biology. Current Opinion in Cell Biology. 63. 194–203. 25 indexed citations
7.
Shi, Ming, Sheng Tan, Ao Li, et al.. (2020). Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data. BMC Genomics. 21(1). 711–711. 1 indexed citations
8.
9.
Li, Yiqun, Ying Wu, Xiaohan Zhang, et al.. (2019). SCIA: A Novel Gene Set Analysis Applicable to Data With Different Characteristics. Frontiers in Genetics. 10. 598–598. 2 indexed citations
10.
Zhang, Hongfang, Zhenzhen Jiang, Yue Jing, et al.. (2018). Cancer-associated Fibroblast–promoted LncRNA DNM3OS Confers Radioresistance by Regulating DNA Damage Response in Esophageal Squamous Cell Carcinoma. Clinical Cancer Research. 25(6). 1989–2000. 128 indexed citations
11.
Shi, Ming, et al.. (2017). Atypical Chemokine Receptor 1 Polymorphism can not Affect Susceptibility to Hepatitis C Virus. Balkan Medical Journal. 34(4). 308–312.
12.
Yao, Yuanfei, et al.. (2015). MARVELD1 modulates cell surface morphology and suppresses epithelial–mesenchymal transition in non‐small cell lung cancer. Molecular Carcinogenesis. 55(11). 1714–1727. 14 indexed citations
13.
Shi, Ming, Yifan Zhang, Leyuan Liu, et al.. (2015). MAP1S Protein Regulates the Phagocytosis of Bacteria and Toll-like Receptor (TLR) Signaling. Journal of Biological Chemistry. 291(3). 1243–1250. 20 indexed citations
14.
Shi, Ming, et al.. (2015). Импортин-β1играет ключевую роль в процессе ядерно-цитоплазматического транспорта MARVELD1. Молекулярная биология. 49(3). 491–497. 2 indexed citations
15.
Shi, Ming, Shan Wang, Yuanfei Yao, et al.. (2014). Biological and clinical significance of epigenetic silencing of MARVELD1 gene in lung cancer. Scientific Reports. 4(1). 7545–7545. 15 indexed citations
16.
Lin, Hu, Zheng Zhang, Ming Shi, et al.. (2012). Evaluation on the efficacy of human umbilical cord derived-mesenchymal stem cell transplantation in liver cirrhosis patients with ascites in a prospective and control trial. 30(4). 204–208. 3 indexed citations
17.
Ren, Weiguo, Ming Shi, Zhenwen Liu, et al.. (2012). Characteristics and significance of peripheral blood Th17 cells and regulatory T cells in liver transplantation patients with acute rejection. SHILAP Revista de lepidopterología. 37(4). 253–257. 1 indexed citations
18.
Tang, Zirong, Ming Shi, Bing Zhang, et al.. (2011). Effect of treatment of autologous cytokine-induced killer cells(CIKs) on the suppression of hepatitis B virus and its mechanism. Jiefangjun yixue zazhi. 36(9). 901–903.
19.
Shi, Ming, Evelyne Gozal, Henry A. Choy, & Henry Jay Forman. (1993). Extracellular glutathione and γ-glutamyl transpeptidase prevent H2O2-induced injury by 2,3-dimethoxy-1,4-naphthoquinone. Free Radical Biology and Medicine. 15(1). 57–67. 89 indexed citations
20.
Meyts, Ewa Rajpert‐De, Ming Shi, Timothy W. Robison, et al.. (1992). Transfection with γ-glutamyl transpeptidase enhances recovery from glutathione depletion using extracellular glutathione. Toxicology and Applied Pharmacology. 114(1). 56–62. 54 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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