Ming‐Ming Hu

3.4k total citations
49 papers, 2.7k citations indexed

About

Ming‐Ming Hu is a scholar working on Immunology, Molecular Biology and Cancer Research. According to data from OpenAlex, Ming‐Ming Hu has authored 49 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Immunology, 22 papers in Molecular Biology and 11 papers in Cancer Research. Recurrent topics in Ming‐Ming Hu's work include interferon and immune responses (33 papers), Immune Response and Inflammation (14 papers) and RNA regulation and disease (8 papers). Ming‐Ming Hu is often cited by papers focused on interferon and immune responses (33 papers), Immune Response and Inflammation (14 papers) and RNA regulation and disease (8 papers). Ming‐Ming Hu collaborates with scholars based in China and Iran. Ming‐Ming Hu's co-authors include Hong‐Bing Shu, Heng Lin, Yan‐Yi Wang, Jing Zhang, Qing Yang, Xueqin Xie, Qing Yang, Tiantian Liu, Shu Li and Bo Zhong and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Advanced Materials and Journal of Biological Chemistry.

In The Last Decade

Ming‐Ming Hu

48 papers receiving 2.7k 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‐Ming Hu China 26 2.0k 1.4k 619 431 352 49 2.7k
Sky W. Brubaker United States 13 1.6k 0.8× 1.4k 1.0× 378 0.6× 311 0.7× 148 0.4× 14 2.6k
Shu Li China 31 2.5k 1.3× 1.9k 1.3× 977 1.6× 512 1.2× 459 1.3× 79 4.2k
Carina C. de Oliveira Mann Germany 16 1.8k 0.9× 1.4k 1.0× 650 1.1× 230 0.5× 113 0.3× 19 2.4k
Nelson O. Gekara Sweden 25 1.4k 0.7× 1.2k 0.8× 514 0.8× 284 0.7× 184 0.5× 40 2.7k
Weiming Yuan United States 24 1.7k 0.9× 882 0.6× 380 0.6× 710 1.6× 124 0.4× 44 2.5k
Shijun J. Zheng China 24 985 0.5× 961 0.7× 323 0.5× 531 1.2× 449 1.3× 68 2.3k
Kithiganahalli Narayanaswamy Balaji India 34 1.3k 0.7× 1.1k 0.8× 1.2k 2.0× 971 2.3× 338 1.0× 81 3.1k
Margaret A. Scull United States 20 1.1k 0.5× 1.2k 0.9× 387 0.6× 803 1.9× 186 0.5× 37 2.5k
Reiko Hirai Japan 18 2.2k 1.1× 1.5k 1.0× 494 0.8× 570 1.3× 266 0.8× 41 3.3k
Jun Ogasawara Japan 17 921 0.5× 1.6k 1.1× 293 0.5× 535 1.2× 257 0.7× 37 2.7k

Countries citing papers authored by Ming‐Ming Hu

Since Specialization
Citations

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

Fields of papers citing papers by Ming‐Ming Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming‐Ming Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Ming‐Ming Hu. A scholar is included among the top collaborators of Ming‐Ming Hu 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‐Ming Hu. Ming‐Ming Hu 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.
Xie, Huan, Zi‐Zi Hu, Ming‐Ming Hu, et al.. (2025). Improving the stability of fish gelatin emulsion by microbial transglutaminase and selected polysaccharides in a steady state. LWT. 232. 118429–118429. 1 indexed citations
2.
Zhou, Xiaoyue, Nianchao Zhang, Xin Sun, et al.. (2025). The carcinogenic metabolite acetaldehyde impairs cGAS activity to negatively regulate antiviral and antitumor immunity. Cancer Letters. 617. 217615–217615. 2 indexed citations
3.
4.
Su, Shan, Xianan Zhang, Lei Bai, et al.. (2022). Modulation of innate immune response to viruses including SARS-CoV-2 by progesterone. Signal Transduction and Targeted Therapy. 7(1). 137–137. 28 indexed citations
5.
He, Wen-Rui, et al.. (2021). VRK2 is involved in the innate antiviral response by promoting mitostress-induced mtDNA release. Cellular and Molecular Immunology. 18(5). 1186–1196. 41 indexed citations
6.
Guo, Wei, Jin Wei, Ru Zang, et al.. (2021). Correction to: SNX8 modulates the innate immune response to RNA viruses by regulating the aggregation of VISA. Cellular and Molecular Immunology. 18(6). 1613–1614. 1 indexed citations
7.
Gao, Peng, Ming‐Ming Hu, & Hong‐Bing Shu. (2020). CSK promotes innate immune response to DNA virus by phosphorylating MITA. Biochemical and Biophysical Research Communications. 526(1). 199–205. 11 indexed citations
8.
Wei, Jin, Huan Lian, Wei Guo, et al.. (2018). SNX8 modulates innate immune response to DNA virus by mediating trafficking and activation of MITA. PLoS Pathogens. 14(10). e1007336–e1007336. 34 indexed citations
9.
Lian, Huan, Jin Wei, Ru Zang, et al.. (2018). ZCCHC3 is a co-sensor of cGAS for dsDNA recognition in innate immune response. Nature Communications. 9(1). 3349–3349. 115 indexed citations
10.
Liu, Ying, Qian Zhou, Li Zhong, et al.. (2018). ZDHHC11 modulates innate immune response to DNA virus by mediating MITA–IRF3 association. Cellular and Molecular Immunology. 15(10). 907–916. 30 indexed citations
11.
Lian, Huan, Ru Zang, Jin Wei, et al.. (2018). The Zinc-Finger Protein ZCCHC3 Binds RNA and Facilitates Viral RNA Sensing and Activation of the RIG-I-like Receptors. Immunity. 49(3). 438–448.e5. 110 indexed citations
12.
Yang, Qing, Tiantian Liu, Heng Lin, et al.. (2017). TRIM32-TAX1BP1-dependent selective autophagic degradation of TRIF negatively regulates TLR3/4-mediated innate immune responses. PLoS Pathogens. 13(9). e1006600–e1006600. 96 indexed citations
13.
Hu, Ming‐Ming & Hong‐Bing Shu. (2017). Multifaceted roles of TRIM38 in innate immune and inflammatory responses. Cellular and Molecular Immunology. 14(4). 331–338. 75 indexed citations
14.
Zhou, Lu, Ming‐Ming Hu, Mi Li, et al.. (2017). PKACs attenuate innate antiviral response by phosphorylating VISA and priming it for MARCH5-mediated degradation. PLoS Pathogens. 13(9). e1006648–e1006648. 33 indexed citations
15.
Hu, Ming‐Ming, Qing Yang, Xueqin Xie, et al.. (2016). Sumoylation Promotes the Stability of the DNA Sensor cGAS and the Adaptor STING to Regulate the Kinetics of Response to DNA Virus. Immunity. 45(3). 555–569. 305 indexed citations
16.
Rong, Lei, Chi Zhang, Qi Lei, et al.. (2016). Hydrogen peroxide detection with high specificity in living cells and inflamed tissues. Regenerative Biomaterials. 3(4). 217–222. 17 indexed citations
17.
He, Jiabei, Ying Hu, Ming‐Ming Hu, Siyi Zhang, & Baolan Li. (2016). The relationship between the preoperative plasma level of HIF-1α and clinic pathological features, prognosis in non-small cell lung cancer. Scientific Reports. 6(1). 20586–20586. 20 indexed citations
18.
Qin, Yue, Maotian Zhou, Ming‐Ming Hu, et al.. (2014). RNF26 Temporally Regulates Virus-Triggered Type I Interferon Induction by Two Distinct Mechanisms. PLoS Pathogens. 10(9). e1004358–e1004358. 167 indexed citations
19.
Yan, Jun, Qijie Li, Aiping Mao, Ming‐Ming Hu, & Hong‐Bing Shu. (2014). TRIM4 modulates type I interferon induction and cellular antiviral response by targeting RIG-I for K63-linked ubiquitination. Journal of Molecular Cell Biology. 6(2). 154–163. 170 indexed citations
20.
Zhang, Jing, Ming‐Ming Hu, Yan‐Yi Wang, & Hong‐Bing Shu. (2012). TRIM32 Protein Modulates Type I Interferon Induction and Cellular Antiviral Response by Targeting MITA/STING Protein for K63-linked Ubiquitination. Journal of Biological Chemistry. 287(34). 28646–28655. 326 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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