Tong Ruan
- Artificial Intelligence top 5%
- Molecular Biology
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Yangming ZhouPing HeQi WangJu GaoDaqi GaoQi YeJing ZhangJiahui Qiu
- Topics
- Topic Modeling (21 papers)Natural Language Processing Techniques (21 papers)Biomedical Text Mining and Ontologies (21 papers)
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Tong Ruan
49 papers receiving 531 citations
Peers
Comparison fields: 5 of 89
- Artificial Intelligence 385
- Molecular Biology 173
- Computer Vision and Pattern Recognition 76
- Information Systems 49
- Radiology, Nuclear Medicine and Imaging 45
Countries citing papers authored by Tong Ruan
This map shows the geographic impact of Tong Ruan'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 Tong Ruan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tong Ruan more than expected).
Fields of papers citing papers by Tong Ruan
This network shows the impact of papers produced by Tong Ruan. 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 Tong Ruan. The network helps show where Tong Ruan may publish in the future.
Co-authorship network of co-authors of Tong Ruan
This figure shows the co-authorship network connecting the top 25 collaborators of Tong Ruan. A scholar is included among the top collaborators of Tong Ruan 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 Tong Ruan. Tong Ruan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 3 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 63 | |
| 7 | 1 | |
| 8 | 2 | |
| 9 | 0 | |
| 10 | 1 | |
| 11 | 0 | |
| 12 | 28 | |
| 13 | 14 | |
| 14 | Recurrent Capsule Network for Relations Extraction: A Practical Application to the Severity Classification of Coronary Artery Disease | 2 |
| 15 | 15 | |
| 16 | Entity Relation Extraction Based on Rule Inference Engine | 1 |
| 17 | Self-Supervised Synonym Extraction from the Web * | 8 |
| 18 | KBMetrics - A Multi-purpose Tool for Measuring Quality of Linked Open Data Sets. | 1 |
| 19 | 10 | |
| 20 | Building and Exploring Marine Oriented Knowledge Graph for ZhouShan Library. | 1 |
About Tong Ruan
Tong Ruan is a scholar working on Artificial Intelligence, Anatomy and Medical Laboratory Technology, having authored 64 papers that have together received 543 indexed citations. Recurring topics across this work include Topic Modeling (21 papers), Natural Language Processing Techniques (21 papers) and Biomedical Text Mining and Ontologies (21 papers). The work is most often cited by research in Artificial Intelligence (385 citations), Health Informatics (13 citations) and Health Information Management (41 citations). Tong Ruan has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Yangming Zhou, Ping He, Qi Wang, Ju Gao, Daqi Gao, Qi Ye, Jing Zhang, Jiahui Qiu, Huanhuan Zhang and Zhiyuan Ma. Their work appears in journals such as Bioinformatics, IEEE Access 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.