Kun Xu

2.0k citations
29 papers · 630 indexed · 1 hit paper · h-index 10
Topics
SARS-CoV-2 and COVID-19 Research (15 papers)Respiratory viral infections research (4 papers)COVID-19 Clinical Research Studies (4 papers)
Partner nations
ChinaCzechiaMacao

In The Last Decade

Kun Xu

25 papers receiving 616 citations

Hit Papers

T-cell immunity of SARS-CoV: Implications for vaccine dev...2016202620192022201650100150200250

Peers

Kun Xu
Comparison fields: 5 of 77
  • Infectious Diseases 500
  • Molecular Biology 159
  • Neurology 115
  • Epidemiology 101
  • Immunology 82
Replace Thomas Theo Brehm with:
Thomas Theo Brehm Germany
Owen TY Tsang Hong Kong
Ronald LW Ko Hong Kong
Zoë L. Lyski United States
Marie-Josée Wendling France
Sarah Shalhoub Canada
Liise-anne Pirofski United States
Alvin X. Han Netherlands
Hebah A. Al-Khatib Qatar
Susannah Leach Sweden
Kun Xu relative to Thomas Theo Brehm Germany Thomas Theo Brehm's profile →
Citations per field
00.5×9.5×
Thomas Theo Brehm · 1×
Citations per year

Countries citing papers authored by Kun Xu

Since Specialization
Citations

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

Fields of papers citing papers by Kun Xu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kun Xu

This figure shows the co-authorship network connecting the top 25 collaborators of Kun Xu. A scholar is included among the top collaborators of Kun Xu 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 Kun Xu. Kun Xu 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
#WorkIndexed citations
1 0
2 2
3 8
4 2
5 0
6 2
7 11
8 1
9 5
10 4
11 7
12 5
13 11
14 29
15 22
16 21
17 95
18 30
19
Opportunities and Challenges of Tight Oil Development in China
1
20
Genetic divergence and phylogeny of Chinese Xinjiang wild rape, Sinapis arvensis and Brassica nigra
4

About Kun Xu

Kun Xu is a scholar working on Infectious Diseases, Health and Modeling and Simulation, having authored 29 papers that have together received 630 indexed citations. Recurring topics across this work include SARS-CoV-2 and COVID-19 Research (15 papers), Respiratory viral infections research (4 papers) and COVID-19 Clinical Research Studies (4 papers). The work is most often cited by research in Infectious Diseases (500 citations), Neurology (115 citations) and Modeling and Simulation (24 citations). Kun Xu has collaborated with scholars based in China, Czechia and Macao. Frequent co-authors include George F. Gao, William J. Liu, Kefang Liu, Min Zhao, Wenjie Tan, Gary Wong, Lianpan Dai, Yi Shi, Yuhai Bi and Yuxuan Han. Their work appears in journals such as ACS Nano, Nature Immunology and Journal of Virology.

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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