Pengwei Xing
Impact in
- Microbiology top 5%
- Antimicrobial Peptides and Activities
- Molecular Biology top 10%
- Machine Learning in Bioinformatics
- RNA and protein synthesis mechanisms
- Genomics and Phylogenetic Studies
- RNA modifications and cancer
- vaccines and immunoinformatics approaches
Papers in
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- Cancer-related molecular mechanisms research 3
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- RNA modifications and cancer 8
- Machine Learning in Bioinformatics 4
- Genomics and Chromatin Dynamics 4
- Genomics and Phylogenetic Studies 3
- RNA and protein synthesis mechanisms 3
- RNA Research and Splicing 3
Pengwei Xing
19 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 119
- Microbiology 109
- Molecular Biology 1.2k
- Cancer Research 235
- Computational Theory and Mathematics 136
- Artificial Intelligence 91
Countries citing papers authored by Pengwei Xing
This map shows the geographic impact of Pengwei Xing'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 Pengwei Xing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pengwei Xing more than expected).
Fields of papers citing papers by Pengwei Xing
This network shows the impact of papers produced by Pengwei Xing. 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 Pengwei Xing. The network helps show where Pengwei Xing may publish in the future.
Co-authors
The 25 scholars most cited alongside Pengwei Xing, 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 | 2024 | 2 | |
| 2 | 2023 | 3 | |
| 3 | 2022 | 15 | |
| 4 | 2022 | 23 | |
| 5 | 2022 | 2 | |
| 6 | 2022 | 5 | |
| 7 | 2022 | 0 | |
| 8 | 2021 | 35 | |
| 9 | 2021 | 9 | |
| 10 | 2021 | 32 | |
| 11 | 2021 | 14 | |
| 12 | Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA Hit paper breakdown → | 2018 | 428 |
| 13 | 2018 | 21 | |
| 14 | 2017 | 104 | |
| 15 | 2017 | 176 | |
| 16 | 2017 | 161 | |
| 17 | 2017 | 7 | |
| 18 | 2017 | 206 | |
| 19 | 2017 | 77 | |
| 20 | 2017 | 107 |
About Pengwei Xing
Pengwei Xing is a scholar working on Cancer Research, Molecular Biology, Microbiology, Clinical Biochemistry and Biochemistry, having authored 20 papers that have together received 1.4k indexed citations. Recurring topics across this work include RNA modifications and cancer (8 papers), Machine Learning in Bioinformatics (4 papers), Genomics and Chromatin Dynamics (4 papers), Genomics and Phylogenetic Studies (3 papers), Cancer-related molecular mechanisms research (3 papers), RNA and protein synthesis mechanisms (3 papers), RNA Research and Splicing (3 papers) and Computational Drug Discovery Methods (2 papers). The work is most often cited by research in Microbiology (109 citations), Molecular Biology (1.2k citations), Cancer Research (235 citations), Computational Theory and Mathematics (136 citations) and Artificial Intelligence (91 citations). Pengwei Xing has collaborated with scholars based in China, Sweden and Germany. Frequent co-authors include Leyi Wei, Quan Zou, Bin Liu, Ran Su, Gaotao Shi, Fei Guo, Wei Chen, Zhi‐Liang Ji, Zhanshan Sam and Jijun Tang. Their work appears in journals such as Scientific Reports, Nature Communications, Artificial Intelligence in Medicine, Cell Death Discovery and Journal of Proteome Research.
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.