Dong‐Jun Yu
- Computational Theory and Mathematics top 0.2%
- Computational Drug Discovery Methods 31
- Rough Sets and Fuzzy Logic 13
- Molecular Biology top 2%
- Machine Learning in Bioinformatics 85
- RNA and protein synthesis mechanisms 50
- Protein Structure and Dynamics 40
- Genomics and Phylogenetic Studies 29
- vaccines and immunoinformatics approaches 12
- Microbiology top 2%
- Artificial Intelligence top 2%
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- Face and Expression Recognition 14
- Cited by
- Computational Theory and MathematicsMolecular BiologyManagement Science and Operations Research
- Journals
- Journal of Chemical Information and Modeling (19 papers)Briefings in Bioinformatics (16 papers)Analytical Biochemistry (12 papers)
- Partner nations
- ChinaAustraliaUnited States
In The Last Decade
Dong‐Jun Yu
145 papers receiving 4.4k citations
Hit Papers
Peers
Comparison fields: 5 of 140
- Computational Theory and Mathematics 1.5k
- Molecular Biology 2.8k
- Management Science and Operations Research 494
- Microbiology 217
- Artificial Intelligence 774
Countries citing papers authored by Dong‐Jun Yu
This map shows the geographic impact of Dong‐Jun Yu'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 Dong‐Jun Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dong‐Jun Yu more than expected).
Fields of papers citing papers by Dong‐Jun Yu
This network shows the impact of papers produced by Dong‐Jun Yu. 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 Dong‐Jun Yu. The network helps show where Dong‐Jun Yu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Dong‐Jun Yu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2024 | 15 | |
| 6 | 2024 | 12 | |
| 7 | 2024 | 2 | |
| 8 | 2024 | 12 | |
| 9 | 2024 | 4 | |
| 10 | 2024 | 13 | |
| 11 | 2024 | 1 | |
| 12 | 2023 | 12 | |
| 13 | 2023 | 2 | |
| 14 | 2023 | 13 | |
| 15 | 2023 | 11 | |
| 16 | 2022 | 18 | |
| 17 | 2022 | 14 | |
| 18 | 2021 | 59 | |
| 19 | Combination of interval-valued fuzzy set and soft setbreakdown → | 2009 | 368 |
| 20 | 2005 | 15 |
About Dong‐Jun Yu
Dong‐Jun Yu is a scholar working on Computational Theory and Mathematics, Molecular Biology, Biophysics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 153 papers that have together received 4.5k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (85 papers), RNA and protein synthesis mechanisms (50 papers), Protein Structure and Dynamics (40 papers), Computational Drug Discovery Methods (31 papers), Genomics and Phylogenetic Studies (29 papers), Face and Expression Recognition (14 papers), Rough Sets and Fuzzy Logic (13 papers) and vaccines and immunoinformatics approaches (12 papers). The work is most often cited by research in Computational Theory and Mathematics (1.5k citations), Molecular Biology (2.8k citations), Management Science and Operations Research (494 citations), Microbiology (217 citations) and Artificial Intelligence (774 citations). Dong‐Jun Yu has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Jingyu Yang, Xibei Yang, Jun Hu, Hong‐Bin Shen, Yang Zhang, Muhammad Arif, Jiangning Song, Yan Li, Tsau Young Lin and Muhammad Kabir. Their work appears in journals such as Journal of Chemical Information and Modeling, Briefings in Bioinformatics, Analytical Biochemistry, IEEE/ACM Transactions on Computational Biology and Bioinformatics 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.