Feifan Liu
- Artificial Intelligence top 2%
- Molecular Biology
- Oncology
- Infectious Diseases top 10%
- Information Systems top 5%
- Co-authors
- Hong YuFei LiuYang LiuWeisong LiuAbhyuday JagannathaYong-gang CaoTsendsuren MunkhdalaiPippa Simpson
- Topics
- Topic Modeling (22 papers)Biomedical Text Mining and Ontologies (13 papers)Natural Language Processing Techniques (10 papers)
- Journals
- Nature CommunicationsJournal of Clinical OncologySHILAP Revista de lepidopterología
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Feifan Liu
90 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 169
- Artificial Intelligence 725
- Molecular Biology 337
- Oncology 132
- Infectious Diseases 121
- Information Systems 117
Countries citing papers authored by Feifan Liu
This map shows the geographic impact of Feifan Liu'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 Feifan Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Feifan Liu more than expected).
Fields of papers citing papers by Feifan Liu
This network shows the impact of papers produced by Feifan Liu. 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 Feifan Liu. The network helps show where Feifan Liu may publish in the future.
Co-authorship network of co-authors of Feifan Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Feifan Liu. A scholar is included among the top collaborators of Feifan Liu 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 Feifan Liu. Feifan Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 19 | |
| 4 | 5 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 2 | |
| 8 | 1 | |
| 9 | 9 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | 16 | |
| 13 | 14 | |
| 14 | 15 | |
| 15 | 26 | |
| 16 | UMass at ImageCLEF Caption Prediction 2018 Task. | 5 |
| 17 | UMass at ImageCLEF Medical Visual Question Answering(Med-VQA) 2018 Task. | 7 |
| 18 | 10 | |
| 19 | Chinese-English Backward Transliteration Assisted with Mining Monolingual Web Pages | 3 |
| 20 | UTDallas at TREC 2008 Blog Track | 5 |
About Feifan Liu
Feifan Liu is a scholar working on Toxicology, Artificial Intelligence and Health Information Management, having authored 100 papers that have together received 1.4k indexed citations. Recurring topics across this work include Topic Modeling (22 papers), Biomedical Text Mining and Ontologies (13 papers) and Natural Language Processing Techniques (10 papers). The work is most often cited by research in Health Informatics (31 citations), Artificial Intelligence (725 citations) and Toxicology (59 citations). Feifan Liu has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Hong Yu, Fei Liu, Yang Liu, Yang Liu, Weisong Liu, Abhyuday Jagannatha, Yong-gang Cao, Tsendsuren Munkhdalai, Pippa Simpson and James J. Cimino. Their work appears in journals such as Nature Communications, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.
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.