Leyi Wei
- Microbiology top 0.5%
- Antimicrobial Peptides and Activities 22
- Molecular Biology top 0.5%
- Machine Learning in Bioinformatics 94
- RNA and protein synthesis mechanisms 50
- vaccines and immunoinformatics approaches 30
- Genomics and Phylogenetic Studies 28
- Protein Structure and Dynamics 20
- RNA modifications and cancer 17
- Computational Theory and Mathematics top 0.2%
- Computational Drug Discovery Methods 34
- Cancer Research top 2%
Leyi Wei
177 papers receiving 8.5k citations
Hit Papers
Peers
Comparison fields: 5 of 171
- Microbiology 870
- Molecular Biology 6.8k
- Computational Theory and Mathematics 1.2k
- Cancer Research 756
- Computer Vision and Pattern Recognition 604
Countries citing papers authored by Leyi Wei
This map shows the geographic impact of Leyi Wei'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 Leyi Wei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leyi Wei more than expected).
Fields of papers citing papers by Leyi Wei
This network shows the impact of papers produced by Leyi Wei. 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 Leyi Wei. The network helps show where Leyi Wei may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Leyi Wei, 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 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 4 | |
| 5 | 2024 | 11 | |
| 6 | 2024 | 6 | |
| 7 | 2024 | 1 | |
| 8 | 2023 | 3 | |
| 9 | 2023 | 75 | |
| 10 | 2023 | 18 | |
| 11 | 2022 | 37 | |
| 12 | 2022 | 111 | |
| 13 | 2020 | 118 | |
| 14 | 2019 | 155 | |
| 15 | 2019 | 33 | |
| 16 | 2019 | 83 | |
| 17 | 2019 | 48 | |
| 18 | ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptidesbreakdown → | 2018 | 369 |
| 19 | 2017 | 104 | |
| 20 | 2017 | 176 |
About Leyi Wei
Leyi Wei is a scholar working on Microbiology, Computational Theory and Mathematics, Molecular Biology, Cancer Research and Biophysics, having authored 199 papers that have together received 8.6k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (94 papers), RNA and protein synthesis mechanisms (50 papers), Computational Drug Discovery Methods (34 papers), vaccines and immunoinformatics approaches (30 papers), Genomics and Phylogenetic Studies (28 papers), Antimicrobial Peptides and Activities (22 papers), Protein Structure and Dynamics (20 papers) and RNA modifications and cancer (17 papers). The work is most often cited by research in Microbiology (870 citations), Molecular Biology (6.8k citations), Computational Theory and Mathematics (1.2k citations), Cancer Research (756 citations) and Computer Vision and Pattern Recognition (604 citations). Leyi Wei has collaborated with scholars based in China, Macao and Japan. Frequent co-authors include Ran Su, Quan Zou, Pengwei Xing, Balachandran Manavalan, Jijun Tang, Shaherin Basith, Gwang Lee, Qiangguo Jin, Tae Hwan Shin and Zhaopeng Meng. Their work appears in journals such as Briefings in Bioinformatics, Bioinformatics, Journal of Chemical Information and Modeling, Computers in Biology and Medicine and Neurocomputing.
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.