Yufan Guo
- Molecular Biology top 10%
- Plant Science top 10%
- Artificial Intelligence top 5%
- Cancer Research
- Rheumatology top 10%
- Co-authors
- Anna KorhonenUlla SteniusIlona SilinsWang‐jin LuJianye ChenJian‐fei KuangWei ShanSimon Baker
- Topics
- Biomedical Text Mining and Ontologies (15 papers)Topic Modeling (14 papers)Natural Language Processing Techniques (7 papers)
- Partner nations
- ChinaUnited KingdomSweden
In The Last Decade
Yufan Guo
76 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 153
- Molecular Biology 749
- Plant Science 303
- Artificial Intelligence 300
- Cancer Research 145
- Rheumatology 126
Countries citing papers authored by Yufan Guo
This map shows the geographic impact of Yufan 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 Yufan Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yufan Guo more than expected).
Fields of papers citing papers by Yufan Guo
This network shows the impact of papers produced by Yufan 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 Yufan Guo. The network helps show where Yufan Guo may publish in the future.
Co-authorship network of co-authors of Yufan Guo
This figure shows the co-authorship network connecting the top 25 collaborators of Yufan Guo. A scholar is included among the top collaborators of Yufan Guo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yufan Guo. Yufan Guo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 5 | |
| 6 | 2 | |
| 7 | 54 | |
| 8 | 10 | |
| 9 | 6 | |
| 10 | 21 | |
| 11 | 7 | |
| 12 | 19 | |
| 13 | 9 | |
| 14 | 87 | |
| 15 | Native Language Identification Using Large, Longitudinal Data | 2 |
| 16 | CRAB 2.0: A text mining tool for supporting literature review in chemical cancer risk assessment | 8 |
| 17 | Improved Information Structure Analysis of Scientific Documents Through Discourse and Lexical Constraints | 8 |
| 18 | CRAB Reader: A Tool for Analysis and Visualization of Argumentative Zones in Scientific Literature | 6 |
| 19 | Using Argumentative Zones for Extractive Summarization of Scientific Articles | 25 |
| 20 | 18 |
About Yufan Guo
Yufan Guo is a scholar working on Obstetrics and Gynecology, Cancer Research and Artificial Intelligence, having authored 84 papers that have together received 1.5k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (15 papers), Topic Modeling (14 papers) and Natural Language Processing Techniques (7 papers). The work is most often cited by research in Health Informatics (15 citations), Molecular Biology (749 citations) and Cancer Research (145 citations). Yufan Guo has collaborated with scholars based in China, United Kingdom and Sweden. Frequent co-authors include Anna Korhonen, Ulla Stenius, Ilona Silins, Wang‐jin Lu, Jianye Chen, Jian‐fei Kuang, Wei Shan, Simon Baker, Johan Högberg and Chaojie Wu. Their work appears in journals such as Bioinformatics, PLoS ONE and The Journal of Clinical Endocrinology & Metabolism.
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.