Fei Guo
- Molecular Biology top 2%
- Machine Learning in Bioinformatics 71
- RNA and protein synthesis mechanisms 32
- Genomics and Phylogenetic Studies 29
- Bioinformatics and Genomic Networks 26
- Protein Structure and Dynamics 23
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods 40
- Cancer Research top 2%
- Cancer-related molecular mechanisms research 18
- MicroRNA in disease regulation 14
- Microbiology top 2%
- Small Animals top 2%
- Journals
- Briefings in Bioinformatics (18 papers)IEEE/ACM Transactions on Computational Biology and Bioinformatics (13 papers)Frontiers in Genetics (6 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Fei Guo
263 papers receiving 5.2k citations
Peers
Comparison fields: 5 of 176
- Molecular Biology 3.7k
- Computational Theory and Mathematics 820
- Cancer Research 697
- Microbiology 178
- Small Animals 147
Countries citing papers authored by Fei Guo
This map shows the geographic impact of Fei Guo'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 Fei Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fei Guo more than expected).
Fields of papers citing papers by Fei Guo
This network shows the impact of papers produced by Fei Guo. 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 Fei Guo. The network helps show where Fei Guo may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Fei Guo, 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 | 1 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 3 | |
| 5 | 2024 | 8 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 9 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 0 | |
| 10 | 2024 | 1 | |
| 11 | 2023 | 36 | |
| 12 | 2023 | 11 | |
| 13 | 2022 | 13 | |
| 14 | 2021 | 38 | |
| 15 | 2018 | 84 | |
| 16 | 2018 | 1 | |
| 17 | High expression of UBD correlates with epirubicin resistance and indicates poor prognosis in triple-negative breast cancer | 2015 | 3 |
| 18 | 2015 | 7 | |
| 19 | 2013 | 19 | |
| 20 | Numerical simulation and experiment research of laser damage of porcelain insulator surface | 2011 | 0 |
About Fei Guo
Fei Guo is a scholar working on Computational Theory and Mathematics, Molecular Biology, Cancer Research, Small Animals and Microbiology, having authored 278 papers that have together received 5.3k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (71 papers), Computational Drug Discovery Methods (40 papers), RNA and protein synthesis mechanisms (32 papers), Genomics and Phylogenetic Studies (29 papers), Bioinformatics and Genomic Networks (26 papers), Protein Structure and Dynamics (23 papers), Cancer-related molecular mechanisms research (18 papers) and MicroRNA in disease regulation (14 papers). The work is most often cited by research in Molecular Biology (3.7k citations), Computational Theory and Mathematics (820 citations), Cancer Research (697 citations), Microbiology (178 citations) and Small Animals (147 citations). Fei Guo has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Jijun Tang, Yijie Ding, Quan Zou, Limin Jiang, Leyi Wei, Yinan Shen, Pengwei Xing, Ran Su, Wenying He and Prayag Tiwari. Their work appears in journals such as Briefings in Bioinformatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Frontiers in Genetics, International Journal of Molecular Sciences and BMC Genomics.
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.