Ran Su
Impact in
- Microbiology top 0.5%
- Antimicrobial Peptides and Activities
- Molecular Biology top 2%
- Machine Learning in Bioinformatics
- RNA and protein synthesis mechanisms
- Genomics and Phylogenetic Studies
- vaccines and immunoinformatics approaches
- RNA modifications and cancer
Papers in
-
- Cell Image Analysis Techniques 9
Ran Su
95 papers receiving 5.4k citations
Hit Papers
Peers
Comparison fields: 5 of 173
- Microbiology 499
- Molecular Biology 3.5k
- Computational Theory and Mathematics 676
- Computer Vision and Pattern Recognition 826
- Radiology, Nuclear Medicine and Imaging 844
Countries citing papers authored by Ran Su
This map shows the geographic impact of Ran Su'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 Ran Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ran Su more than expected).
Fields of papers citing papers by Ran Su
This network shows the impact of papers produced by Ran Su. 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 Ran Su. The network helps show where Ran Su may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ran Su, 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 | 2 | |
| 2 | 2025 | 2 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 3 | |
| 7 | 2023 | 75 | |
| 8 | 2023 | 10 | |
| 9 | DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis Hit paper breakdown → | 2023 | 104 |
| 10 | 2020 | 118 | |
| 11 | 2019 | 155 | |
| 12 | 2019 | 48 | |
| 13 | 2019 | 30 | |
| 14 | 2019 | 144 | |
| 15 | 2019 | 117 | |
| 16 | 2019 | 39 | |
| 17 | ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides Hit paper breakdown → | 2018 | 369 |
| 18 | 2018 | 129 | |
| 19 | 2017 | 176 | |
| 20 | 2017 | 187 |
About Ran Su
Ran Su is a scholar working on Biophysics, Microbiology, Computational Theory and Mathematics, Computer Vision and Pattern Recognition and Molecular Biology, having authored 105 papers that have together received 5.5k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (36 papers), Computational Drug Discovery Methods (15 papers), RNA and protein synthesis mechanisms (13 papers), Genomics and Phylogenetic Studies (12 papers), RNA modifications and cancer (10 papers), Gene expression and cancer classification (10 papers), AI in cancer detection (10 papers) and Cell Image Analysis Techniques (9 papers). The work is most often cited by research in Microbiology (499 citations), Molecular Biology (3.5k citations), Computational Theory and Mathematics (676 citations), Computer Vision and Pattern Recognition (826 citations) and Radiology, Nuclear Medicine and Imaging (844 citations). Ran Su has collaborated with scholars based in China, Australia and Japan. Frequent co-authors include Leyi Wei, Quan Zou, Zhaopeng Meng, Qiangguo Jin, Changming Sun, Tuan D. Pham, Chen Zhou, Qi Chen, Jiangning Song and Pengwei Xing. Their work appears in journals such as Briefings in Bioinformatics, Bioinformatics, Knowledge-Based Systems, Frontiers in Bioengineering and Biotechnology and 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.