Gung‐Wei Chirn

4.9k citations
29 papers · 1.0k indexed · h-index 16
Topics
RNA and protein synthesis mechanisms (6 papers)Algorithms and Data Compression (6 papers)Cancer Genomics and Diagnostics (5 papers)
Partner nations
United StatesChinaCanada

In The Last Decade

Gung‐Wei Chirn

29 papers receiving 1.0k citations

Peers

Gung‐Wei Chirn
Comparison fields: 5 of 98
  • Molecular Biology 656
  • Plant Science 206
  • Computational Theory and Mathematics 167
  • Biophysics 145
  • Artificial Intelligence 110
Replace Cosmin Lazar with:
Cosmin Lazar Belgium
Kirk R. Anders United States
Zhirong Sun China
Gabriella Rustici United Kingdom
Steven B. Haase United States
Michael R. Gryk United States
John S. Burg United States
Dan Du China
Thomas Wild Germany
Jeremy L. Muhlich United States
Gung‐Wei Chirn relative to Cosmin Lazar Belgium Cosmin Lazar's profile →
Citations per field
00.5×5.2×
Cosmin Lazar · 1×
Citations per year

Countries citing papers authored by Gung‐Wei Chirn

Since Specialization
Citations

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

Fields of papers citing papers by Gung‐Wei Chirn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gung‐Wei Chirn. 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 Gung‐Wei Chirn. The network helps show where Gung‐Wei Chirn may publish in the future.

Co-authorship network of co-authors of Gung‐Wei Chirn

This figure shows the co-authorship network connecting the top 25 collaborators of Gung‐Wei Chirn. A scholar is included among the top collaborators of Gung‐Wei Chirn 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 Gung‐Wei Chirn. Gung‐Wei Chirn 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 36
2 3
3 1
4 11
5 82
6 43
7 74
8 9
9 26
10 19
11 48
12 13
13 15
14 266
15 98
16 40
17 52
18 2
19 3
20 31

About Gung‐Wei Chirn

Gung‐Wei Chirn is a scholar working on Cancer Research, Otorhinolaryngology and Virology, having authored 29 papers that have together received 1.0k indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (6 papers), Algorithms and Data Compression (6 papers) and Cancer Genomics and Diagnostics (5 papers). The work is most often cited by research in Biophysics (145 citations), Computational Theory and Mathematics (167 citations) and Molecular Biology (656 citations). Gung‐Wei Chirn has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Nelson C. Lau, Yuliya A. Sytnikova, Kaizhong Zhang, Mark Labow, Thomas G. Marr, Dennis Shasha, Bruce A. Shapiro, Daniel M. Kemp, Andreas Bender and Timothy J. Mitchison. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

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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