Kangkang Guo

632 total citations
46 papers, 458 citations indexed

About

Kangkang Guo is a scholar working on Agronomy and Crop Science, Cardiology and Cardiovascular Medicine and Molecular Biology. According to data from OpenAlex, Kangkang Guo has authored 46 papers receiving a total of 458 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Agronomy and Crop Science, 15 papers in Cardiology and Cardiovascular Medicine and 11 papers in Molecular Biology. Recurrent topics in Kangkang Guo's work include Animal Disease Management and Epidemiology (30 papers), Viral Infections and Immunology Research (15 papers) and Animal Virus Infections Studies (11 papers). Kangkang Guo is often cited by papers focused on Animal Disease Management and Epidemiology (30 papers), Viral Infections and Immunology Research (15 papers) and Animal Virus Infections Studies (11 papers). Kangkang Guo collaborates with scholars based in China, Spain and Taiwan. Kangkang Guo's co-authors include Yanming Zhang, Huifang Lv, Kai Kang, Wulong Liang, Zhi Cao, Pengbo Ning, Tao Wang, Chengbao Wang, Jihui Lin and Qizhuang Lv and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of Virology.

In The Last Decade

Kangkang Guo

43 papers receiving 451 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kangkang Guo China 14 204 149 134 107 105 46 458
Huanjie Zhai China 13 71 0.3× 57 0.4× 51 0.4× 114 1.1× 80 0.8× 28 332
Chuyu Zhang China 12 145 0.7× 139 0.9× 123 0.9× 57 0.5× 77 0.7× 55 480
Dajun Zhang China 11 190 0.9× 118 0.8× 81 0.6× 61 0.6× 104 1.0× 38 406
Qingxia Lu China 11 50 0.2× 85 0.6× 58 0.4× 142 1.3× 144 1.4× 29 349
Lilan Xie China 13 89 0.4× 95 0.6× 119 0.9× 184 1.7× 144 1.4× 27 491
Qizu Zhao China 12 262 1.3× 122 0.8× 242 1.8× 77 0.7× 11 0.1× 31 379
Elizabeth A. Schafer United States 12 109 0.5× 132 0.9× 157 1.2× 29 0.3× 131 1.2× 15 409
Jinke Yang China 7 156 0.8× 84 0.6× 54 0.4× 47 0.4× 82 0.8× 23 301
Sushant Bhat United Kingdom 11 131 0.6× 119 0.8× 54 0.4× 54 0.5× 67 0.6× 27 389

Countries citing papers authored by Kangkang Guo

Since Specialization
Citations

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

Fields of papers citing papers by Kangkang Guo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kangkang Guo

This figure shows the co-authorship network connecting the top 25 collaborators of Kangkang Guo. A scholar is included among the top collaborators of Kangkang 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 Kangkang Guo. Kangkang 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.
Zhao, Long, Dong Wang, Liang Zhang, et al.. (2025). Isolation and characterization of bovine coronavirus variants with mutations in the hemagglutinin-esterase gene in dairy calves in China. BMC Veterinary Research. 21(1). 92–92. 2 indexed citations
2.
Wang, Dong, Long Zhao, Jingyi Lin, et al.. (2024). Analysis of Characteristics of Bovine-Derived Non-Enterotoxigenic Bacteroides fragilis and Validation of Potential Probiotic Effects. Microorganisms. 12(11). 2319–2319.
3.
Wang, Tao, Yaru Liu, Ying Sun, et al.. (2022). Rab22a cooperates with Rab5 and NS4B in classical swine fever virus entry process. Veterinary Microbiology. 266. 109363–109363. 2 indexed citations
4.
Guo, Kangkang, et al.. (2022). Analysis of Tangential Combustion Instability Modes in a LOX/Kerosene Liquid Rocket Engine Based on OpenFOAM. Frontiers in Energy Research. 9. 7 indexed citations
5.
Guo, Kangkang, et al.. (2021). Numerical investigation of spray self-pulsation characteristics of liquid-centered swirl coaxial injector with different recess lengths. International Journal of Multiphase Flow. 138. 103592–103592. 7 indexed citations
6.
Zhang, Liang, et al.. (2021). ARFGAP1 binds to classical swine fever virus NS5A protein and enhances CSFV replication in PK-15 cells. Veterinary Microbiology. 255. 109034–109034. 7 indexed citations
7.
Gao, Zhen, Han Zhang, Xiaohui Wei, et al.. (2020). Reliable Classification with Ensemble Convolutional Neural Networks. 1–4. 6 indexed citations
8.
Wang, Yifan, Hongqing Zheng, Dong Wang, et al.. (2020). Antiviral activity of ISG15 against classical swine fever virus replication in porcine alveolar macrophages via inhibition of autophagy by ISGylating BECN1. Veterinary Research. 51(1). 22–22. 24 indexed citations
9.
Wang, Dong, Huifang Lv, Cheng Li, et al.. (2018). MAVS induces a host cell defense to inhibit CSFV infection. Archives of Virology. 163(7). 1805–1821. 15 indexed citations
10.
Gui, Qian, Huifang Lv, Jihui Lin, et al.. (2018). FHC, an NS4B-interacting Protein, Enhances Classical Swine Fever Virus Propagation and Acts Positively in Viral Anti-apoptosis. Scientific Reports. 8(1). 8318–8318. 7 indexed citations
11.
Lin, Jihui, Chengbao Wang, Tao Wang, et al.. (2017). Rab5 Enhances Classical Swine Fever Virus Proliferation and Interacts with Viral NS4B Protein to Facilitate Formation of NS4B Related Complex. Frontiers in Microbiology. 8. 1468–1468. 18 indexed citations
12.
Lv, Huifang, Jie Wang, Xiaomeng Li, et al.. (2017). uS10, a novel Npro-interacting protein, inhibits classical swine fever virus replication. Journal of General Virology. 98(7). 1679–1692. 20 indexed citations
13.
Lv, Huifang, Dong Wang, Zhi Cao, et al.. (2017). TRAF6 is a novel NS3-interacting protein that inhibits classical swine fever virus replication. Scientific Reports. 7(1). 6737–6737. 25 indexed citations
14.
Guo, Kangkang, Haimin Li, Mengmeng Wu, et al.. (2017). Molecular chaperone Jiv promotes the RNA replication of classical swine fever virus. Virus Genes. 53(3). 426–433. 4 indexed citations
15.
Ning, Pengbo, Lifang Gao, Yulu Zhou, et al.. (2016). Caveolin-1-mediated endocytic pathway is involved in classical swine fever virus Shimen infection of porcine alveolar macrophages. Veterinary Microbiology. 195. 81–86. 28 indexed citations
16.
Li, Helin, Chengcheng Zhang, Kangkang Guo, et al.. (2016). FKBP8 interact with classical swine fever virus NS5A protein and promote virus RNA replication. Virus Genes. 52(1). 99–106. 11 indexed citations
17.
Li, Weiwei, Gang Wang, Wulong Liang, et al.. (2014). Integrin β3 Is Required in Infection and Proliferation of Classical Swine Fever Virus. PLoS ONE. 9(10). e110911–e110911. 21 indexed citations
18.
Mo, Zhihong, et al.. (2007). A nanogold-quenched fluorescence duplex probe for homogeneous DNA detection based on strand displacement. Analytical and Bioanalytical Chemistry. 389(2). 493–497. 15 indexed citations
19.
Zhang, Yanming, et al.. (2007). Cytopathic Effect of Classical Swine Fever Virus NS3 Protein on PK-15 Cells. Intervirology. 50(6). 433–438. 8 indexed citations
20.
Satake, Takaaki, et al.. (1986). Studies on pelleting biomass. II. Compression characteristics during formation of pellets and wafers.. Journal of the Japanese Society of Agricultural Machinery. 48(1). 83–90. 1 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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