Scott Bowes
Impact in
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- Computational Drug Discovery Methods
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- NF-κB Signaling Pathways
Papers in
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- Computational Drug Discovery Methods 7
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- Animal testing and alternatives 2
- Co-authors
- Serene Josiah (6 shared papers)Wen‐Cherng Lee (4 shared papers)Juswinder Singh (4 shared papers)Leona E. Ling (3 shared papers)Miki N. Newman (3 shared papers)Claudio E. Chuaqui (3 shared papers)Robert M. Arduini (2 shared papers)Hung Kam Cheung (2 shared papers)
- Journals
- SLAS DISCOVERY (4 papers)Bioorganic & Medicinal Chemistry Letters (1 paper)Arteriosclerosis Thrombosis and Vascular Biology (1 paper)Journal of Investigative Dermatology (1 paper)Structure (1 paper)
- Partner nations
- United StatesHungary
In The Last Decade
Scott Bowes
10 papers receiving 457 citations
Peers
Comparison fields: 5 of 61
- Computational Theory and Mathematics 130
- Cancer Research 79
- Molecular Biology 164
- Pathology and Forensic Medicine 38
- Biophysics 12
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 | 151 | |
| 2 | 2008 | 110 | |
| 3 | 2006 | 80 | |
| 4 | 2008 | 77 | |
| 5 | 2006 | 16 | |
| 6 | 2006 | 15 | |
| 7 | 2005 | 14 | |
| 8 | 2013 | 10 | |
| 9 | 2016 | 6 | |
| 10 | 2008 | 5 |
About Scott Bowes
Scott Bowes is a scholar working on Computational Theory and Mathematics, Small Animals, Molecular Biology, Statistics, Probability and Uncertainty and Analytical Chemistry, having authored 10 papers that have together received 484 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (7 papers), TGF-β signaling in diseases (3 papers), Animal testing and alternatives (2 papers), Analytical Methods in Pharmaceuticals (1 paper), Chemical Synthesis and Analysis (1 paper), Advanced Biosensing Techniques and Applications (1 paper), NF-κB Signaling Pathways (1 paper) and Reliability and Agreement in Measurement (1 paper). The work is most often cited by research in Computational Theory and Mathematics (130 citations), Cancer Research (79 citations), Molecular Biology (164 citations), Pathology and Forensic Medicine (38 citations) and Biophysics (12 citations). Scott Bowes has collaborated with scholars based in United States and Hungary. Frequent co-authors include Serene Josiah, Wen‐Cherng Lee, Juswinder Singh, Leona E. Ling, Miki N. Newman, Claudio E. Chuaqui, Robert M. Arduini, Hung Kam Cheung, P. Ann Boriack‐Sjodin and Timothy W. Pontz. Their work appears in journals such as SLAS DISCOVERY, Bioorganic & Medicinal Chemistry Letters, Arteriosclerosis Thrombosis and Vascular Biology, Journal of Investigative Dermatology and Structure.
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.