Lin Deng
Impact in
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- Neuroscience and Neuropharmacology Research
Papers in
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- Gene expression and cancer classification 3
- Machine Learning in Bioinformatics 2
- Bioinformatics and Genomic Networks 2
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- Web Data Mining and Analysis 2
- Co-authors
- Dik Lun Lee (4 shared papers)Xiaoyong Chai (2 shared papers)Qiang Yang (1 shared paper)Charles X. Ling (1 shared paper)Jian Pei (2 shared papers)Gong Chen (1 shared paper)Min Jiang (1 shared paper)Wilfred Ng (3 shared papers)
- Journals
- Molecular Biology of the Cell (1 paper)ACM Transactions on Internet Technology (1 paper)Gene Therapy (1 paper)Journal of Neuroscience (1 paper)Computer Networks (1 paper)
- Partner nations
- Hong KongChinaUnited States
In The Last Decade
Lin Deng
10 papers receiving 360 citations
Peers
Comparison fields: 5 of 88
- Neurology 33
- Cellular and Molecular Neuroscience 68
- Information Systems 81
- Artificial Intelligence 101
- Cognitive Neuroscience 56
Countries citing papers authored by Lin Deng
This map shows the geographic impact of Lin Deng'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 Lin Deng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lin Deng more than expected).
Fields of papers citing papers by Lin Deng
This network shows the impact of papers produced by Lin Deng. 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 Lin Deng. The network helps show where Lin Deng may publish in the future.
Co-authors
The 24 scholars most cited alongside Lin Deng, 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 | 2014 | 100 | |
| 2 | 2005 | 82 | |
| 3 | 2004 | 55 | |
| 4 | 2004 | 53 | |
| 5 | 2005 | 25 | |
| 6 | 2007 | 23 | |
| 7 | 2004 | 18 | |
| 8 | Spying Out Real User Preferences for Metasearch Engine Personalization. | 2004 | 7 |
| 9 | 2017 | 4 | |
| 10 | 2004 | 4 | |
| 11 | 2025 | 0 |
About Lin Deng
Lin Deng is a scholar working on Molecular Biology, Information Systems, Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 371 indexed citations. Recurring topics across this work include Gene expression and cancer classification (3 papers), Machine Learning in Bioinformatics (2 papers), Bioinformatics and Genomic Networks (2 papers), Web Data Mining and Analysis (2 papers), interferon and immune responses (1 paper), Complex Network Analysis Techniques (1 paper), Video Surveillance and Tracking Methods (1 paper) and Transcranial Magnetic Stimulation Studies (1 paper). The work is most often cited by research in Neurology (33 citations), Cellular and Molecular Neuroscience (68 citations), Information Systems (81 citations), Artificial Intelligence (101 citations) and Cognitive Neuroscience (56 citations). Lin Deng has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Dik Lun Lee, Xiaoyong Chai, Qiang Yang, Charles X. Ling, Jian Pei, Gong Chen, Min Jiang, Wilfred Ng, Jinwen Ma and Joshua I. Glaser. Their work appears in journals such as Molecular Biology of the Cell, ACM Transactions on Internet Technology, Gene Therapy, Journal of Neuroscience and Computer Networks.
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.