Tom Ryder
Impact in
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- Gene expression and cancer classification
- Advanced biosensing and bioanalysis techniques
- Protein Kinase Regulation and GTPase Signaling
- RNA and protein synthesis mechanisms
- Genomics and Chromatin Dynamics
- Molecular Biology Techniques and Applications
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- Virus-based gene therapy research
Papers in
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- Gene expression and cancer classification 2
- Gene Regulatory Network Analysis 1
- Advanced Biosensing Techniques and Applications 1
- Advanced biosensing and bioanalysis techniques 1
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- Image and Signal Denoising Methods 1
- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Nobuo Tsuchida (1 shared paper)Eiichi Ohtsubo (1 shared paper)Rui Mei (2 shared papers)Teresa Webster (2 shared papers)Weimin Liu (1 shared paper)Fred C. Christians (1 shared paper)Paul Kaplan (1 shared paper)David Kulp (1 shared paper)
- Journals
- Proceedings of the National Academy of Sciences (1 paper)Science (1 paper)Technology and Culture (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (1 paper)
- Partner nations
- United StatesSweden
In The Last Decade
Tom Ryder
5 papers receiving 301 citations
Peers
Comparison fields: 5 of 61
- Molecular Biology 242
- Genetics 78
- Oncology 65
- Cancer Research 28
- Neurology 18
Countries citing papers authored by Tom Ryder
This map shows the geographic impact of Tom Ryder'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 Tom Ryder with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Ryder more than expected).
Fields of papers citing papers by Tom Ryder
This network shows the impact of papers produced by Tom Ryder. 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 Tom Ryder. The network helps show where Tom Ryder may publish in the future.
Co-authors
The 15 scholars most cited alongside Tom Ryder, 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 | 1982 | 213 | |
| 2 | 2003 | 103 | |
| 3 | 2001 | 12 | |
| 4 | 2022 | 6 | |
| 5 | 1980 | 2 | |
| 6 | Working with the Environment | 1996 | 1 |
About Tom Ryder
Tom Ryder is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition, Oncology, Genetics and Immunology, having authored 6 papers that have together received 337 indexed citations. Recurring topics across this work include Gene expression and cancer classification (2 papers), T-cell and Retrovirus Studies (1 paper), Viral-associated cancers and disorders (1 paper), Gene Regulatory Network Analysis (1 paper), Advanced Biosensing Techniques and Applications (1 paper), Image and Signal Denoising Methods (1 paper), Advanced biosensing and bioanalysis techniques (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). The work is most often cited by research in Molecular Biology (242 citations), Genetics (78 citations), Oncology (65 citations), Cancer Research (28 citations) and Neurology (18 citations). Tom Ryder has collaborated with scholars based in United States and Sweden. Frequent co-authors include Nobuo Tsuchida, Eiichi Ohtsubo, Rui Mei, Teresa Webster, Weimin Liu, Fred C. Christians, Paul Kaplan, David Kulp, Stefan Bekiranov and Earl Hubbell. Their work appears in journals such as Proceedings of the National Academy of Sciences, Science, Technology and Culture, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.
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.