Runyu Jing
Impact in
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- Antimicrobial Peptides and Activities
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- Computational Drug Discovery Methods
Papers in
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- Machine Learning in Bioinformatics 18
- RNA and protein synthesis mechanisms 9
- Bioinformatics and Genomic Networks 8
- RNA modifications and cancer 7
- Genomics and Phylogenetic Studies 6
- Gene expression and cancer classification 6
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- Computational Drug Discovery Methods 6
- Co-authors
- Jiesi Luo (14 shared papers)Yizhou Li (15 shared papers)Lezheng Yu (12 shared papers)Fengjuan Liu (6 shared papers)Menglong Li (26 shared papers)Xue Li (7 shared papers)Zhining Wen (6 shared papers)Yonglin Zhang (7 shared papers)
- Journals
- Chemometrics and Intelligent Laboratory Systems (5 papers)Frontiers in Endocrinology (3 papers)Frontiers in Microbiology (3 papers)Scientific Reports (2 papers)Analytical Methods (2 papers)
- Partner nations
- ChinaUnited States
In The Last Decade
Runyu Jing
42 papers receiving 310 citations
Peers
Comparison fields: 5 of 79
- Microbiology 23
- Computational Theory and Mathematics 57
- Molecular Biology 247
- Cancer Research 27
- Biophysics 9
Countries citing papers authored by Runyu Jing
This map shows the geographic impact of Runyu Jing'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 Runyu Jing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Runyu Jing more than expected).
Fields of papers citing papers by Runyu Jing
This network shows the impact of papers produced by Runyu Jing. 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 Runyu Jing. The network helps show where Runyu Jing may publish in the future.
Co-authors
The 25 scholars most cited alongside Runyu Jing, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 69 | |
| 2 | 2022 | 33 | |
| 3 | 2020 | 23 | |
| 4 | 2014 | 18 | |
| 5 | 2021 | 12 | |
| 6 | 2015 | 12 | |
| 7 | 2022 | 11 | |
| 8 | 2021 | 11 | |
| 9 | 2022 | 10 | |
| 10 | 2014 | 9 | |
| 11 | 2020 | 8 | |
| 12 | 2016 | 8 | |
| 13 | 2015 | 8 | |
| 14 | 2011 | 7 | |
| 15 | 2023 | 6 | |
| 16 | 2017 | 5 | |
| 17 | 2024 | 5 | |
| 18 | 2021 | 5 | |
| 19 | 2023 | 4 | |
| 20 | 2025 | 4 |
About Runyu Jing
Runyu Jing is a scholar working on Molecular Biology, Computational Theory and Mathematics, Cancer Research, Artificial Intelligence and Genetics, having authored 46 papers that have together received 314 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (18 papers), RNA and protein synthesis mechanisms (9 papers), Bioinformatics and Genomic Networks (8 papers), RNA modifications and cancer (7 papers), Genomics and Phylogenetic Studies (6 papers), Computational Drug Discovery Methods (6 papers), Cancer-related molecular mechanisms research (6 papers) and Gene expression and cancer classification (6 papers). The work is most often cited by research in Microbiology (23 citations), Computational Theory and Mathematics (57 citations), Molecular Biology (247 citations), Cancer Research (27 citations) and Biophysics (9 citations). Runyu Jing has collaborated with scholars based in China and United States. Frequent co-authors include Jiesi Luo, Yizhou Li, Lezheng Yu, Fengjuan Liu, Menglong Li, Xue Li, Zhining Wen, Yonglin Zhang, Fengjuan Liu and Yuan Liu. Their work appears in journals such as Chemometrics and Intelligent Laboratory Systems, Frontiers in Endocrinology, Frontiers in Microbiology, Scientific Reports and Analytical Methods.
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.