Jae-June Dong

610 citations
27 papers · 422 indexed · h-index 13
Topics
Computational Drug Discovery Methods (8 papers)SARS-CoV-2 and COVID-19 Research (6 papers)vaccines and immunoinformatics approaches (3 papers)

In The Last Decade

Jae-June Dong

26 papers receiving 415 citations

Peers

Jae-June Dong
Comparison fields: 5 of 86
  • Molecular Biology 183
  • Infectious Diseases 106
  • Computational Theory and Mathematics 95
  • Epidemiology 57
  • Organic Chemistry 48
Replace Jean-Baptiste Mazarati with:
Jean-Baptiste Mazarati Rwanda
Francesca Alessandra Ambrosio Italy
Mohd Imran Khan India
Norah A. Alturki Saudi Arabia
Fulin Lian China
Inés Maestro Spain
Amr Alhossary Singapore
Siddharth Sinha Macao
Yanyan Diao China
Saleh Alshamrani Saudi Arabia
Jae-June Dong relative to Jean-Baptiste Mazarati Rwanda Jean-Baptiste Mazarati's profile →
Citations per field
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Jean-Baptiste Mazarati · 1×
Citations per year

Countries citing papers authored by Jae-June Dong

Since Specialization
Citations

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

Fields of papers citing papers by Jae-June Dong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jae-June Dong. 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 Jae-June Dong. The network helps show where Jae-June Dong may publish in the future.

Co-authorship network of co-authors of Jae-June Dong

This figure shows the co-authorship network connecting the top 25 collaborators of Jae-June Dong. A scholar is included among the top collaborators of Jae-June Dong 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 Jae-June Dong. Jae-June Dong 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 18
4 6
5 10
6 9
7 12
8 30
9 9
10 1
11 14
12 20
13 15
14 16
15 22
16 66
17 12
18 19
19 1
20 6

About Jae-June Dong

Jae-June Dong is a scholar working on Computational Theory and Mathematics, Clinical Biochemistry and Infectious Diseases, having authored 27 papers that have together received 422 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (8 papers), SARS-CoV-2 and COVID-19 Research (6 papers) and vaccines and immunoinformatics approaches (3 papers). The work is most often cited by research in Computational Theory and Mathematics (95 citations), Infectious Diseases (106 citations) and Health Informatics (4 citations). Jae-June Dong has collaborated with scholars based in South Korea, Saudi Arabia and India. Frequent co-authors include Mohammad Hassan Baig, Mohd Imran Khan, Irfan Ahmad, Abd‐ElAziem Farouk, Young Goo Song, Tanuj Sharma, Zainul A. Khan, Imran Khan, Md. Imtaiyaz Hassan and Mustafa Aziz Hatiboğlu. Their work appears in journals such as PLoS ONE, Cancer Research and International Journal of Molecular Sciences.

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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