Junru Jin
Impact in
- Microbiology top 10%
- Antimicrobial Peptides and Activities
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- Computational Drug Discovery Methods
Papers in
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- Machine Learning in Bioinformatics 13
- RNA and protein synthesis mechanisms 8
- RNA modifications and cancer 6
- Protein Structure and Dynamics 5
- Epigenetics and DNA Methylation 4
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- Computational Drug Discovery Methods 6
- Co-authors
- Leyi Wei (22 shared papers)Ruheng Wang (5 shared papers)Ran Su (7 shared papers)Quan Zou (5 shared papers)Kenta Nakai (4 shared papers)Zhongshen Li (9 shared papers)Yi Jiang (4 shared papers)Yingying Yu (4 shared papers)
- Journals
- Computers in Biology and Medicine (4 papers)Bioinformatics (4 papers)Briefings in Bioinformatics (4 papers)Nature Communications (2 papers)Journal of Chemical Information and Modeling (2 papers)
- Partner nations
- ChinaSouth KoreaMacao
In The Last Decade
Junru Jin
22 papers receiving 649 citations
Junru Jin's Hit Papers
Peers
Comparison fields: 5 of 69
- Microbiology 67
- Computational Theory and Mathematics 135
- Molecular Biology 552
- Cancer Research 71
- Artificial Intelligence 66
Countries citing papers authored by Junru Jin
This map shows the geographic impact of Junru Jin'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 Junru Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junru Jin more than expected).
Fields of papers citing papers by Junru Jin
This network shows the impact of papers produced by Junru Jin. 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 Junru Jin. The network helps show where Junru Jin may publish in the future.
Co-authors
The 25 scholars most cited alongside Junru Jin, 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 24 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis Hit paper breakdown → | 2023 | 104 |
| 2 | 2022 | 95 | |
| 3 | 2023 | 75 | |
| 4 | 2022 | 62 | |
| 5 | 2023 | 55 | |
| 6 | 2021 | 45 | |
| 7 | 2021 | 38 | |
| 8 | 2022 | 37 | |
| 9 | 2024 | 29 | |
| 10 | 2023 | 18 | |
| 11 | 2022 | 18 | |
| 12 | 2023 | 15 | |
| 13 | 2023 | 15 | |
| 14 | 2023 | 14 | |
| 15 | 2023 | 10 | |
| 16 | 2023 | 8 | |
| 17 | 2024 | 5 | |
| 18 | 2023 | 3 | |
| 19 | 2024 | 3 | |
| 20 | 2025 | 2 |
About Junru Jin
Junru Jin is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Cancer Research and Artificial Intelligence, having authored 24 papers that have together received 654 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (13 papers), RNA and protein synthesis mechanisms (8 papers), Computational Drug Discovery Methods (6 papers), Machine Learning in Materials Science (6 papers), RNA modifications and cancer (6 papers), Protein Structure and Dynamics (5 papers), Epigenetics and DNA Methylation (4 papers) and Cancer-related molecular mechanisms research (4 papers). The work is most often cited by research in Microbiology (67 citations), Computational Theory and Mathematics (135 citations), Molecular Biology (552 citations), Cancer Research (71 citations) and Artificial Intelligence (66 citations). Junru Jin has collaborated with scholars based in China, South Korea and Macao. Frequent co-authors include Leyi Wei, Ruheng Wang, Ran Su, Quan Zou, Kenta Nakai, Zhongshen Li, Yi Jiang, Yingying Yu, Balachandran Manavalan and Wenjia He. Their work appears in journals such as Computers in Biology and Medicine, Bioinformatics, Briefings in Bioinformatics, Nature Communications and Journal of Chemical Information and Modeling.
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.