Guan‐Yu Chen

1.2k citations
51 papers · 901 indexed · h-index 14
Topics
Viral Infectious Diseases and Gene Expression in Insects (10 papers)Markov Chains and Monte Carlo Methods (9 papers)Virus-based gene therapy research (6 papers)
Partner nations
TaiwanUnited StatesChina

In The Last Decade

Guan‐Yu Chen

48 papers receiving 888 citations

Peers

Guan‐Yu Chen
Comparison fields: 5 of 114
  • Molecular Biology 555
  • Genetics 170
  • Cancer Research 167
  • Biomedical Engineering 122
  • Statistics and Probability 73
Replace Li Li Hsiao with:
Li Li Hsiao United States
Robyn P. Araujo Australia
Cesar A. Vargas-García United States
Peng Qiu China
Xiaojing Zheng United States
Shunsuke Tomita Japan
Ami Radunskaya United States
Nathalie Malo Canada
David Van Valen United States
H Nomura Japan
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Citations per field
00.5×11.1×
Li Li Hsiao · 1×
Citations per year

Countries citing papers authored by Guan‐Yu Chen

Since Specialization
Citations

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

Fields of papers citing papers by Guan‐Yu Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guan‐Yu Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Guan‐Yu Chen. A scholar is included among the top collaborators of Guan‐Yu Chen 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 Guan‐Yu Chen. Guan‐Yu Chen 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 1
3 1
4 2
5 2
6 1
7 1
8 1
9 8
10
BUPT-MCPRL at TRECVID 2019: ActEV and INS.
2
11 31
12
On the mixing time and spectral gap for birth and death chains
8
13 1
14 115
15 7
16 46
17 28
18 5
19 5
20 5

About Guan‐Yu Chen

Guan‐Yu Chen is a scholar working on Statistics and Probability, Geometry and Topology and Mathematical Physics, having authored 51 papers that have together received 901 indexed citations. Recurring topics across this work include Viral Infectious Diseases and Gene Expression in Insects (10 papers), Markov Chains and Monte Carlo Methods (9 papers) and Virus-based gene therapy research (6 papers). The work is most often cited by research in Cancer Research (167 citations), Statistics and Probability (73 citations) and Molecular Biology (555 citations). Guan‐Yu Chen has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Chi‐Yuan Chen, Laurent Saloff‐Coste, Chin‐Yu Lin, Yu-Chen Hu, Shiaw-Min Hwang, Yu‐Chen Hu, Jaw‐Ching Wu, Yu‐Der Lee, Dar‐Fu Tai and Margaret Dah‐Tsyr Chang. Their work appears in journals such as Biomaterials, Analytical Chemistry and Journal of Cleaner Production.

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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