Scott Bowes
Impact in
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- NF-κB Signaling Pathways
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- Computational Drug Discovery Methods
Papers in
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- TGF-β signaling in diseases 3
- Viral Infectious Diseases and Gene Expression in Insects 2
- Cancer-related gene regulation 2
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- Computational Drug Discovery Methods 4
- Co-authors
- Serene Josiah (6 shared papers)Juswinder Singh (4 shared papers)Wen‐Cherng Lee (4 shared papers)Leona Ling (3 shared papers)Miki Newman (3 shared papers)Claudio Chuaqui (3 shared papers)P. Ann Boriack‐Sjodin (2 shared papers)Robert M. Arduini (2 shared papers)
- Journals
- SLAS DISCOVERY (4 papers)Bioorganic & Medicinal Chemistry Letters (1 paper)Journal of Investigative Dermatology (1 paper)Structure (1 paper)Chemical Biology & Drug Design (1 paper)
- Partner nations
- United StatesSwitzerlandChina
In The Last Decade
Scott Bowes
9 papers receiving 387 citations
Peers
Comparison fields: 5 of 72
- Cancer Research 88
- Computational Theory and Mathematics 92
- Molecular Biology 246
- Immunology 69
- Immunology and Allergy 15
Countries citing papers authored by Scott Bowes
This map shows the geographic impact of Scott Bowes'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 Scott Bowes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott Bowes more than expected).
Fields of papers citing papers by Scott Bowes
This network shows the impact of papers produced by Scott Bowes. 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 Scott Bowes. The network helps show where Scott Bowes may publish in the future.
Co-authors
The 25 scholars most cited alongside Scott Bowes, 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 | 2003 | 149 | |
| 2 | 2008 | 110 | |
| 3 | 2006 | 78 | |
| 4 | 2005 | 16 | |
| 5 | 2006 | 16 | |
| 6 | 2006 | 15 | |
| 7 | 2013 | 10 | |
| 8 | 2016 | 5 | |
| 9 | 2008 | 4 |
About Scott Bowes
Scott Bowes is a scholar working on Molecular Biology, Computational Theory and Mathematics, Oncology, Pharmacology and Pathology and Forensic Medicine, having authored 9 papers that have together received 403 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), TGF-β signaling in diseases (3 papers), Viral Infectious Diseases and Gene Expression in Insects (2 papers), Pancreatic and Hepatic Oncology Research (2 papers), Cancer-related gene regulation (2 papers), NF-κB Signaling Pathways (1 paper), Biosimilars and Bioanalytical Methods (1 paper) and Statistical Methods in Clinical Trials (1 paper). The work is most often cited by research in Cancer Research (88 citations), Computational Theory and Mathematics (92 citations), Molecular Biology (246 citations), Immunology (69 citations) and Immunology and Allergy (15 citations). Scott Bowes has collaborated with scholars based in United States, Switzerland and China. Frequent co-authors include Serene Josiah, Juswinder Singh, Wen‐Cherng Lee, Leona Ling, Miki Newman, Claudio Chuaqui, P. Ann Boriack‐Sjodin, Robert M. Arduini, Timothy Pontz and Hung Kam Cheung. Their work appears in journals such as SLAS DISCOVERY, Bioorganic & Medicinal Chemistry Letters, Journal of Investigative Dermatology, Structure and Chemical Biology & Drug Design.
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.