Jun Jiao

1.7k citations
58 papers · 968 · h-index 18

Impact in

Papers in

    • Liver Disease Diagnosis and Treatment 5
    • Influenza Virus Research Studies 4
    • Respiratory viral infections research 3
    • Circular RNAs in diseases 3

Jun Jiao

55 papers receiving 958 citations

Peers

Jun Jiao
Comparison fields: 5 of 133
  • Cancer Research 216
  • Modeling and Simulation 31
  • Molecular Biology 369
  • Physiology 115
  • Nutrition and Dietetics 58
Replace Yanyan Wang with:
Yanyan Wang China
Bo Pang China
Dong Tian China
Jorge L. Sepulveda United States
Jingxuan Wang China
Xiaoshuang Liu China
Kert Viele United States
Jun Jiao relative to Yanyan Wang China Yanyan Wang's profile →
Citations per field
00.5×4.6×
Yanyan Wang · 1×
Citations per year

Countries citing papers authored by Jun Jiao

Since Specialization
Citations

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

Fields of papers citing papers by Jun Jiao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Jiao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Jiao Line = papers co-authored together Jun Jiao links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017161
2 201985
3 201567
4 201654
5 201553
6 201649
7 201647
8 202036
9 202134
10 202425
11 201624
12 201722
13 200922
14 201822
15 201918
16 202117
17 202217
18 202017
19 202314
20 202313

About Jun Jiao

Jun Jiao is a scholar working on Epidemiology, Molecular Biology, Infectious Diseases, Agronomy and Crop Science and Surgery, having authored 58 papers that have together received 968 indexed citations. Recurring topics across this work include Liver Disease Diagnosis and Treatment (5 papers), Animal Disease Management and Epidemiology (5 papers), Influenza Virus Research Studies (4 papers), COVID-19 epidemiological studies (3 papers), Cholesterol and Lipid Metabolism (3 papers), Vaccine Coverage and Hesitancy (3 papers), Respiratory viral infections research (3 papers) and Circular RNAs in diseases (3 papers). The work is most often cited by research in Cancer Research (216 citations), Modeling and Simulation (31 citations), Molecular Biology (369 citations), Physiology (115 citations) and Nutrition and Dietetics (58 citations). Jun Jiao has collaborated with scholars based in China, United States and Italy. Frequent co-authors include Li‐Qiang Qin, Jiaying Xu, Daoxin Ma, Baoxia Cui, Weiguo Zhang, Shufen Han, Chunyan Ji, Wei Li, Peng Li and Chaoqin Zhong. Their work appears in journals such as Frontiers in Public Health, Journal of X-Ray Science and Technology, International Journal for Equity in Health, Food & Nutrition Research and Scientific Reports.

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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