Dokyun Na

5.0k citations
84 papers · 3.6k indexed · 3 hit papers · h-index 25
Topics
Computational Drug Discovery Methods (13 papers)RNA and protein synthesis mechanisms (12 papers)CRISPR and Genetic Engineering (11 papers)

In The Last Decade

Dokyun Na

80 papers receiving 3.6k citations

Hit Papers

Systems metabolic engineering of microorganisms for natur...2012202620162021201220132021100200300400500

Peers

Dokyun Na
Comparison fields: 5 of 150
  • Molecular Biology 2.7k
  • Biomedical Engineering 698
  • Computational Theory and Mathematics 445
  • Genetics 411
  • Cancer Research 248
Replace Hao Liu with:
Hao Liu China
Jan Brezovský Czechia
Yong Zhou China
Yang Cao China
William P. Janzen United States
Xiujun Zhang China
Ziding Zhang China
Petr Beneš Czechia
Qin Chu China
Dokyun Na relative to Hao Liu China Hao Liu's profile →
Citations per field
00.5×3.1×
Hao Liu · 1×
Citations per year

Countries citing papers authored by Dokyun Na

Since Specialization
Citations

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

Fields of papers citing papers by Dokyun Na

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dokyun Na

This figure shows the co-authorship network connecting the top 25 collaborators of Dokyun Na. A scholar is included among the top collaborators of Dokyun Na 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 Dokyun Na. Dokyun Na 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 0
3 2
4 0
5 1
6 4
7 16
8 15
9 9
10 14
11
In silico methods and tools for drug discoverybreakdown →
352
12 18
13 139
14 10
15 18
16 21
17
Systems metabolic engineering of microorganisms for natural and non-natural chemicalsbreakdown →
553
18 103
19 4
20
Fuzzy continuous petri net-based approach for modeling immune systems
3

About Dokyun Na

Dokyun Na is a scholar working on Molecular Biology, Computational Theory and Mathematics and Developmental Neuroscience, having authored 84 papers that have together received 3.6k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (13 papers), RNA and protein synthesis mechanisms (12 papers) and CRISPR and Genetic Engineering (11 papers). The work is most often cited by research in Molecular Biology (2.7k citations), Computational Theory and Mathematics (445 citations) and Cancer Research (248 citations). Dokyun Na has collaborated with scholars based in South Korea, Canada and Pakistan. Frequent co-authors include Sang Yup Lee, Seung Min Yoo, Bilal Shaker, Doheon Lee, Jingyu Lee, Jong Myoung Park, Sol Choi, Jeong Wook Lee, Joungmin Lee and Hannah Chung. Their work appears in journals such as Nucleic Acids Research, Nature Biotechnology and Bioinformatics.

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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