Kun Chen

10.0k citations
317 papers · 6.4k · 2 hit papers · h-index 37

Impact in

Papers in

Kun Chen

292 papers receiving 6.2k citations

Kun Chen's Hit Papers

Characteristics of COVID-19 infection in Beijing 2020 · 783 citations
7830+2+4Years since publication250500750

Peers

Kun Chen
Comparison fields: 5 of 211
  • Computational Mathematics 50
  • Statistics and Probability 482
  • Modeling and Simulation 258
  • Soil Science 392
  • Biological Psychiatry 102
Replace Heping Zhang with:
Heping Zhang United States
Eran Segal Israel
Hongyu Zhao United States
Lennart Eriksson Sweden
Jorge Cadima Portugal
Xihong Lin United States
Hongzhe Li United States
Elisa T. Lee United States
Kjell Johnson United States
Thomas M. Loughin United States
Kun Chen relative to Heping Zhang United States Heping Zhang's profile →
Citations per field
00.5×3.5×
Heping Zhang · 1×
Citations per year

Countries citing papers authored by Kun Chen

Since Specialization
Citations

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

Fields of papers citing papers by Kun Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Kun Chen, 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 Kun Chen Line = papers co-authored together Kun Chen links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 317 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Characteristics of COVID-19 infection in Beijing
Hit paper breakdown →
2020783
2 2006249
3
Altered gut microbial profile is associated with abnormal metabolism activity of Autism Spectrum Disorder
Hit paper breakdown →
2020241
4 2019195
5 2013164
6 2019139
7 2015128
8 2021113
9 1999104
10 201699
11 202096
12 199494
13 201690
14 202089
15 201782
16 201178
17 202075
18 202074
19 202170
20 202168

About Kun Chen

Kun Chen is a scholar working on Molecular Biology, Statistics and Probability, Artificial Intelligence, Epidemiology and Clinical Psychology, having authored 317 papers that have together received 6.4k indexed citations. Recurring topics across this work include Statistical Methods and Inference (32 papers), Soil Carbon and Nitrogen Dynamics (19 papers), Statistical Methods and Bayesian Inference (16 papers), Gut microbiota and health (15 papers), Advanced Statistical Methods and Models (15 papers), Suicide and Self-Harm Studies (15 papers), Bayesian Methods and Mixture Models (13 papers) and Climate change and permafrost (11 papers). The work is most often cited by research in Computational Mathematics (50 citations), Statistics and Probability (482 citations), Modeling and Simulation (258 citations), Soil Science (392 citations) and Biological Psychiatry (102 citations). Kun Chen has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Kalok Chan, Haobin Dong, Gui Jin, Huixin Lian, Nan Hu, Jianren Li, Dali Wang, Hui Chen, Shengmei Niu and Xuqin Kang. Their work appears in journals such as PLoS ONE, Biometrika, Scientific Reports, The Annals of Applied Statistics and Cold Regions Science and Technology.

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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