Shaoxiong Yu

723 total citations · 1 hit paper
16 papers, 559 citations indexed

About

Shaoxiong Yu is a scholar working on Agronomy and Crop Science, Cardiology and Cardiovascular Medicine and Infectious Diseases. According to data from OpenAlex, Shaoxiong Yu has authored 16 papers receiving a total of 559 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Agronomy and Crop Science, 9 papers in Cardiology and Cardiovascular Medicine and 7 papers in Infectious Diseases. Recurrent topics in Shaoxiong Yu's work include Animal Disease Management and Epidemiology (14 papers), Viral Infections and Immunology Research (9 papers) and Animal Virus Infections Studies (6 papers). Shaoxiong Yu is often cited by papers focused on Animal Disease Management and Epidemiology (14 papers), Viral Infections and Immunology Research (9 papers) and Animal Virus Infections Studies (6 papers). Shaoxiong Yu collaborates with scholars based in China, United Kingdom and Pakistan. Shaoxiong Yu's co-authors include Yuan Sun, Hua‐Ji Qiu, Lian‐Feng Li, Hailiang Ge, Jinghan Wang, Yongfeng Li, Su Li, Yuzi Luo, Su Li and Li Su and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Virology and Frontiers in Microbiology.

In The Last Decade

Shaoxiong Yu

16 papers receiving 552 citations

Hit Papers

The A137R Protein of Afri... 2022 2026 2023 2024 2022 25 50 75

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shaoxiong Yu China 14 314 176 168 167 164 16 559
Gisselle N. Medina United States 16 321 1.0× 145 0.8× 139 0.8× 316 1.9× 232 1.4× 40 771
Hongxing Ding China 16 453 1.4× 190 1.1× 111 0.7× 252 1.5× 215 1.3× 42 749
Helen L. Johns United Kingdom 13 284 0.9× 122 0.7× 88 0.5× 253 1.5× 130 0.8× 14 628
Changyao Li China 16 345 1.1× 335 1.9× 239 1.4× 125 0.7× 193 1.2× 22 837
Xiangping Yin China 14 195 0.6× 141 0.8× 65 0.4× 148 0.9× 128 0.8× 48 522
Pingping Zhou China 13 208 0.7× 269 1.5× 262 1.6× 66 0.4× 177 1.1× 28 830
Peili Cao China 5 437 1.4× 167 0.9× 61 0.4× 235 1.4× 327 2.0× 10 607
Jacqueline L. Cavender United States 12 409 1.3× 190 1.1× 124 0.7× 97 0.6× 251 1.5× 15 611
Qinghong Xue China 13 127 0.4× 177 1.0× 146 0.9× 65 0.4× 57 0.3× 36 457
Marisa Nogal Spain 11 628 2.0× 251 1.4× 111 0.7× 324 1.9× 454 2.8× 11 785

Countries citing papers authored by Shaoxiong Yu

Since Specialization
Citations

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

Fields of papers citing papers by Shaoxiong Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shaoxiong Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Shaoxiong Yu. A scholar is included among the top collaborators of Shaoxiong Yu 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 Shaoxiong Yu. Shaoxiong Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Zhang, Kehui, Hailiang Ge, Pingping Zhou, et al.. (2023). The D129L protein of African swine fever virus interferes with the binding of transcriptional coactivator p300 and IRF3 to prevent beta interferon induction. Journal of Virology. 97(10). e0082423–e0082423. 8 indexed citations
2.
Ge, Hailiang, Yànhuá Lǐ, Kehui Zhang, et al.. (2023). The E301R protein of African swine fever virus functions as a sliding clamp involved in viral genome replication. mBio. 14(5). e0164523–e0164523. 9 indexed citations
3.
Zheng, Yuxuan, Su Li, Shihua Li, et al.. (2022). Transcriptome profiling in swine macrophages infected with African swine fever virus at single-cell resolution. Proceedings of the National Academy of Sciences. 119(19). e2201288119–e2201288119. 67 indexed citations
4.
Yu, Shaoxiong, et al.. (2022). Modulation of Macrophage Polarization by Viruses: Turning Off/On Host Antiviral Responses. Frontiers in Microbiology. 13. 839585–839585. 47 indexed citations
5.
Yu, Shaoxiong, Hailiang Ge, Tao Wang, et al.. (2022). The A137R Protein of African Swine Fever Virus Inhibits Type I Interferon Production via the Autophagy-Mediated Lysosomal Degradation of TBK1. Journal of Virology. 96(9). e0195721–e0195721. 90 indexed citations breakdown →
6.
Liu, Ruili, Yeping Sun, Yan Chai, et al.. (2020). The structural basis of African swine fever virus pA104R binding to DNA and its inhibition by stilbene derivatives. Proceedings of the National Academy of Sciences. 117(20). 11000–11009. 36 indexed citations
7.
Li, Lian‐Feng, Yuexiu Zhang, Liang Qu, et al.. (2020). MERTK is a host factor that promotes classical swine fever virus entry and antagonizes innate immune response in PK-15 cells. Emerging Microbes & Infections. 9(1). 571–581. 20 indexed citations
8.
Zhang, Yuexiu, Huawei Zhang, Qian Yang, et al.. (2019). Porcine RING Finger Protein 114 Inhibits Classical Swine Fever Virus Replication via K27-Linked Polyubiquitination of Viral NS4B. Journal of Virology. 93(21). 26 indexed citations
9.
Wang, Jinghan, Yuan Sun, Lian‐Feng Li, et al.. (2018). Comprehensive evaluation of the host responses to infection with differentially virulent classical swine fever virus strains in pigs. Virus Research. 255. 68–76. 14 indexed citations
10.
Yu, Shaoxiong, Caixia Yin, Kun Song, et al.. (2018). Engagement of cellular cholesterol in the life cycle of classical swine fever virus: its potential as an antiviral target. Journal of General Virology. 100(2). 156–165. 17 indexed citations
11.
Li, Su, Jinghan Wang, Qian Yang, et al.. (2017). Complex Virus–Host Interactions Involved in the Regulation of Classical Swine Fever Virus Replication: A Minireview. Viruses. 9(7). 171–171. 32 indexed citations
12.
Wang, Jinghan, Shu‐Cheng Chen, Yajin Liao, et al.. (2016). Mitogen-Activated Protein Kinase Kinase 2, a Novel E2-Interacting Protein, Promotes the Growth of Classical Swine Fever Virus via Attenuation of the JAK-STAT Signaling Pathway. Journal of Virology. 90(22). 10271–10283. 25 indexed citations
13.
Li, Lian‐Feng, Jiahui Yu, Yongfeng Li, et al.. (2016). Guanylate-Binding Protein 1, an Interferon-Induced GTPase, Exerts an Antiviral Activity against Classical Swine Fever Virus Depending on Its GTPase Activity. Journal of Virology. 90(9). 4412–4426. 73 indexed citations
14.
Li, Yongfeng, Lian‐Feng Li, Shaoxiong Yu, et al.. (2016). Applications of Replicating-Competent Reporter-Expressing Viruses in Diagnostic and Molecular Virology. Viruses. 8(5). 127–127. 17 indexed citations
15.
Chen, Jianing, Wen-Rui He, Liang Shen, et al.. (2015). The Laminin Receptor Is a Cellular Attachment Receptor for Classical Swine Fever Virus. Journal of Virology. 89(9). 4894–4906. 61 indexed citations
16.
Li, Su, Shuo Feng, Jinghan Wang, et al.. (2015). eEF1A Interacts with the NS5A Protein and Inhibits the Growth of Classical Swine Fever Virus. Viruses. 7(8). 4563–4581. 17 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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