Karyn L. Sutton
- Modeling and Simulation top 5%
- Mathematical Biology Tumor Growth 3
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- Gene Regulatory Network Analysis 4
- Chemical Synthesis and Analysis 2
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- Pneumonia and Respiratory Infections 5
- Influenza Virus Research Studies 4
- Mycobacterium research and diagnosis 4
- Respiratory viral infections research 3
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- Mathematical and Theoretical Epidemiology and Ecology Models 4
Karyn L. Sutton
22 papers receiving 221 citations
Peers
Comparison fields: 5 of 85
- Modeling and Simulation 41
- Immunology 55
- Biophysics 11
- Computational Mathematics 1
- Molecular Biology 99
Countries citing papers authored by Karyn L. Sutton
This map shows the geographic impact of Karyn L. Sutton'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 Karyn L. Sutton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Karyn L. Sutton more than expected).
Fields of papers citing papers by Karyn L. Sutton
This network shows the impact of papers produced by Karyn L. Sutton. 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 Karyn L. Sutton. The network helps show where Karyn L. Sutton may publish in the future.
Co-authorship network
The 23 scholars most cited alongside Karyn L. Sutton, 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 | 2018 | 2 | |
| 2 | 2017 | 6 | |
| 3 | 2014 | 3 | |
| 4 | 2014 | 4 | |
| 5 | 2013 | 8 | |
| 6 | 2011 | 35 | |
| 7 | Dynamic modeling of behavior change in problem drinkers | 2011 | 3 |
| 8 | 2010 | 11 | |
| 9 | 2010 | 59 | |
| 10 | 2010 | 0 | |
| 11 | 2010 | 6 | |
| 12 | Model formulation of drinking behavior using longitudinal data | 2010 | 1 |
| 13 | Conversion of dynamic social network stochastic differential equation model to Fokker-Planck model | 2009 | 1 |
| 14 | 2009 | 12 | |
| 15 | Inverse problem methods as a public health tool in pneumonoccal vaccination | 2008 | 2 |
| 16 | 2008 | 15 | |
| 17 | An age-structured model for pneumococcal infection with vaccination | 2008 | 1 |
| 18 | 2006 | 10 | |
| 19 | 2004 | 16 | |
| 20 | 2001 | 20 |
About Karyn L. Sutton
Karyn L. Sutton is a scholar working on Modeling and Simulation, Biophysics and Microbiology, having authored 23 papers that have together received 232 indexed citations. Recurring topics across this work include Pneumonia and Respiratory Infections (5 papers), Influenza Virus Research Studies (4 papers), Gene Regulatory Network Analysis (4 papers), Mycobacterium research and diagnosis (4 papers), Mathematical and Theoretical Epidemiology and Ecology Models (4 papers), Respiratory viral infections research (3 papers), Mathematical Biology Tumor Growth (3 papers) and Chemical Synthesis and Analysis (2 papers). The work is most often cited by research in Modeling and Simulation (41 citations), Immunology (55 citations) and Biophysics (11 citations). Karyn L. Sutton has collaborated with scholars based in United States, Spain and Germany. Frequent co-authors include H. T. Banks, W. Clayton Thompson, Andreas Meyerhans, Gennady Bocharov, H. T. Banks, Geneva M. Omann, Dirk Roose, Carlos Castillo‐Chávez, Anna Waller and Jennifer J. Linderman. Their work appears in journals such as Biochemistry, Journal of Immunological Methods and Cellular Signalling.
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.