Chun Tang
- Artificial Intelligence top 5%
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- Face and Expression Recognition 4
- Image Retrieval and Classification Techniques 3
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- Gene expression and cancer classification 14
- Bioinformatics and Genomic Networks 9
- Molecular Biology Techniques and Applications 2
- Genomics and Chromatin Dynamics 1
- Information Systems top 5%
- Data Mining Algorithms and Applications 7
- Signal Processing top 10%
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- Intellectual Property and Patents 2
- Journals
- Bioinformatics (1 paper)Distributed and Parallel Databases (1 paper)Knowledge and Information Systems (1 paper)
- Partner nations
- United StatesCanadaChina
In The Last Decade
Chun Tang
19 papers receiving 959 citations
Hit Papers
Peers
Comparison fields: 5 of 107
- Artificial Intelligence 422
- Computer Vision and Pattern Recognition 240
- Molecular Biology 659
- Information Systems 191
- Signal Processing 73
Countries citing papers authored by Chun Tang
This map shows the geographic impact of Chun Tang'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 Chun Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chun Tang more than expected).
Fields of papers citing papers by Chun Tang
This network shows the impact of papers produced by Chun Tang. 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 Chun Tang. The network helps show where Chun Tang may publish in the future.
Co-authorship network
The 22 scholars most cited alongside Chun Tang, 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 | 5 | |
| 2 | 2011 | 3 | |
| 3 | An Empirical Test of the Evaluation Model on Core Patent Documents | 2011 | 0 |
| 4 | Study on Indicator System for Core Patent Documents Evaluation | 2009 | 7 |
| 5 | 2006 | 11 | |
| 6 | 2005 | 12 | |
| 7 | Cluster analysis for gene expression data: a surveybreakdown → | 2004 | 765 |
| 8 | 2004 | 4 | |
| 9 | 2004 | 45 | |
| 10 | Mining Coherent Gene Clusters from Three-Dimensional Microarray Data ⁄ | 2004 | 1 |
| 11 | 2003 | 9 | |
| 12 | 2003 | 7 | |
| 13 | 2003 | 10 | |
| 14 | 2003 | 11 | |
| 15 | 2003 | 22 | |
| 16 | 2002 | 8 | |
| 17 | 2002 | 7 | |
| 18 | 2002 | 7 | |
| 19 | 2001 | 11 | |
| 20 | 2001 | 93 |
About Chun Tang
Chun Tang is a scholar working on Computer Vision and Pattern Recognition, Energy Engineering and Power Technology and Information Systems, having authored 20 papers that have together received 1.0k indexed citations. Recurring topics across this work include Gene expression and cancer classification (14 papers), Bioinformatics and Genomic Networks (9 papers), Data Mining Algorithms and Applications (7 papers), Face and Expression Recognition (4 papers), Image Retrieval and Classification Techniques (3 papers), Intellectual Property and Patents (2 papers), Molecular Biology Techniques and Applications (2 papers) and Genomics and Chromatin Dynamics (1 paper). The work is most often cited by research in Artificial Intelligence (422 citations), Computer Vision and Pattern Recognition (240 citations) and Molecular Biology (659 citations). Chun Tang has collaborated with scholars based in United States, Canada and China. Frequent co-authors include Daxin Jiang, Aidong Zhang, Aidong Zhang, Murali Ramanathan, Li Zhang, Jian Pei, Aibing Rao, Yuqing Song, Lei Zhu and Chuan Lin. Their work appears in journals such as Bioinformatics, Distributed and Parallel Databases, Knowledge and Information Systems, Renewable Energy and IEEE Transactions on Knowledge and Data Engineering.
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.