David Swigon

2.4k total citations
59 papers, 1.8k citations indexed

About

David Swigon is a scholar working on Molecular Biology, Epidemiology and Genetics. According to data from OpenAlex, David Swigon has authored 59 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 13 papers in Epidemiology and 10 papers in Genetics. Recurrent topics in David Swigon's work include DNA and Nucleic Acid Chemistry (13 papers), RNA and protein synthesis mechanisms (11 papers) and Bacteriophages and microbial interactions (8 papers). David Swigon is often cited by papers focused on DNA and Nucleic Acid Chemistry (13 papers), RNA and protein synthesis mechanisms (11 papers) and Bacteriophages and microbial interactions (8 papers). David Swigon collaborates with scholars based in United States, Austria and South Korea. David Swigon's co-authors include Bernard D. Coleman, Wilma K. Olson, Irwin Tobias, Gilles Clermont, Luke Czapla, Baris Hancioglu, Seth A. Darst, Richard H. Ebright, Catherine L. Lawson and Helen M. Berman and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Chemical Physics and SHILAP Revista de lepidopterología.

In The Last Decade

David Swigon

55 papers receiving 1.7k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David Swigon United States 22 954 302 229 226 189 59 1.8k
Oleg A. Igoshin United States 31 1.5k 1.6× 838 2.8× 345 1.5× 350 1.5× 52 0.3× 95 2.3k
Chung‐ke Chang Taiwan 22 874 0.9× 104 0.3× 209 0.9× 586 2.6× 61 0.3× 42 2.7k
Ariel Amir United States 26 1.2k 1.3× 860 2.8× 240 1.0× 208 0.9× 41 0.2× 86 2.3k
Christian Conrad Germany 23 1.0k 1.1× 199 0.7× 65 0.3× 298 1.3× 172 0.9× 59 2.9k
Charles W. Wolgemuth United States 31 1.1k 1.1× 320 1.1× 258 1.1× 1.0k 4.4× 47 0.2× 85 3.4k
Peter Hagedorn Denmark 29 1.1k 1.2× 100 0.3× 57 0.2× 211 0.9× 76 0.4× 58 2.4k
Claude Loverdo France 19 1.4k 1.5× 232 0.8× 168 0.7× 195 0.9× 69 0.4× 35 1.9k
Chuan Xiao China 24 674 0.7× 283 0.9× 706 3.1× 75 0.3× 291 1.5× 117 2.5k
Thorsten Wagner Germany 16 891 0.9× 119 0.4× 86 0.4× 112 0.5× 53 0.3× 35 1.9k
Sergei Nechaev Russia 20 937 1.0× 355 1.2× 242 1.1× 146 0.6× 17 0.1× 114 2.0k

Countries citing papers authored by David Swigon

Since Specialization
Citations

This map shows the geographic impact of David Swigon'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 David Swigon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Swigon more than expected).

Fields of papers citing papers by David Swigon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Swigon. 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 David Swigon. The network helps show where David Swigon may publish in the future.

Co-authorship network of co-authors of David Swigon

This figure shows the co-authorship network connecting the top 25 collaborators of David Swigon. A scholar is included among the top collaborators of David Swigon based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Swigon. David Swigon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Rubin, Jonathan E., et al.. (2023). Qualitative inverse problems: mapping data to the features of trajectories and parameter values of an ODE model. Inverse Problems. 39(7). 75002–75002. 1 indexed citations
2.
Parker, Robert S., et al.. (2022). Identification of Clinical Phenotypes in Septic Patients Presenting With Hypotension or Elevated Lactate. Frontiers in Medicine. 9. 794423–794423. 12 indexed citations
3.
Clermont, Gilles, et al.. (2022). Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment. SHILAP Revista de lepidopterología. 1(2). e0000012–e0000012. 15 indexed citations
4.
Rubin, Jonathan E., et al.. (2021). A data-driven model of the role of energy in sepsis. Journal of Theoretical Biology. 533. 110948–110948. 3 indexed citations
5.
Rubin, Jonathan E., et al.. (2020). Identification of affine dynamical systems from a single trajectory. Inverse Problems. 36(8). 85004–85004. 2 indexed citations
6.
Swigon, David, et al.. (2018). Slow-fast analysis of a multi-group asset flow model with implications for the dynamics of wealth. PLoS ONE. 13(11). e0207764–e0207764. 2 indexed citations
7.
Clermont, Gilles, et al.. (2017). APT-MCMC, a C++/Python implementation of Markov Chain Monte Carlo for parameter identification. Computers & Chemical Engineering. 110. 1–12. 5 indexed citations
8.
Pike, Francis, et al.. (2015). Sepsis Endotypes Defined By Cytokine Trajectory Analysis. Intensive Care Medicine Experimental. 3(S1). 1 indexed citations
9.
Swigon, David, et al.. (2015). The inflammatory response to influenza A virus (H1N1): An experimental and mathematical study. Journal of Theoretical Biology. 374. 83–93. 39 indexed citations
10.
Swigon, David, et al.. (2015). A Three-Tiered Study of Differences in Murine Intrahost Immune Response to Multiple Pneumococcal Strains. PLoS ONE. 10(8). e0134012–e0134012. 6 indexed citations
11.
DePasse, Jay V., Roni Rosenfeld, Elodie Ghedin, et al.. (2014). A large-scale immuno-epidemiological simulation of influenza A epidemics. BMC Public Health. 14(1). 1019–1019. 32 indexed citations
12.
Caginalp, Gunduz, et al.. (2011). Are flash crashes caused by instabilities arising from rapid trading. SSRN Electronic Journal. 1 indexed citations
13.
Arciero, Julia, Qi Mi, Maria Branca, David J. Hackam, & David Swigon. (2011). Continuum Model of Collective Cell Migration in Wound Healing and Colony Expansion. Biophysical Journal. 100(3). 535–543. 94 indexed citations
14.
Swigon, David, Julia Arciero, Qi Mi, & David J. Hackam. (2010). Continuum Elastic Model of Epithelial Sheet Migration. Biophysical Journal. 98(3). 163a–163a. 2 indexed citations
15.
Rivière, Béatrice, Yekaterina Epshteyn, David Swigon, & Yoram Vodovotz. (2008). A simple mathematical model of signaling resulting from the binding of lipopolysaccharide with Toll-like receptor 4 demonstrates inherent preconditioning behavior. Mathematical Biosciences. 217(1). 19–26. 32 indexed citations
16.
Czapla, Luke, David Swigon, & Wilma K. Olson. (2008). Effects of the Nucleoid Protein HU on the Structure, Flexibility, and Ring-Closure Properties of DNA Deduced from Monte Carlo Simulations. Journal of Molecular Biology. 382(2). 353–370. 29 indexed citations
17.
Mi, Qi, David Swigon, Béatrice Rivière, et al.. (2007). One-Dimensional Elastic Continuum Model of Enterocyte Layer Migration. Biophysical Journal. 93(11). 3745–3752. 21 indexed citations
18.
Swigon, David, Bernard D. Coleman, & Wilma K. Olson. (2006). Modeling the Lac repressor-operator assembly: The influence of DNA looping on Lac repressor conformation. Proceedings of the National Academy of Sciences. 103(26). 9879–9884. 101 indexed citations
19.
Keller, David, David Swigon, & Carlos Bustamante. (2003). Relating Single-Molecule Measurements to Thermodynamics. Biophysical Journal. 84(2). 733–738. 86 indexed citations
20.
Swigon, David, Bernard D. Coleman, & Irwin Tobias. (1998). The Elastic Rod Model for DNA and Its Application to the Tertiary Structure of DNA Minicircles in Mononucleosomes. Biophysical Journal. 74(5). 2515–2530. 66 indexed citations

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.

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