Thomas S. Deisboeck

6.2k total citations
88 papers, 4.4k citations indexed

About

Thomas S. Deisboeck is a scholar working on Modeling and Simulation, Molecular Biology and Cell Biology. According to data from OpenAlex, Thomas S. Deisboeck has authored 88 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Modeling and Simulation, 45 papers in Molecular Biology and 26 papers in Cell Biology. Recurrent topics in Thomas S. Deisboeck's work include Mathematical Biology Tumor Growth (53 papers), Gene Regulatory Network Analysis (31 papers) and Microtubule and mitosis dynamics (16 papers). Thomas S. Deisboeck is often cited by papers focused on Mathematical Biology Tumor Growth (53 papers), Gene Regulatory Network Analysis (31 papers) and Microtubule and mitosis dynamics (16 papers). Thomas S. Deisboeck collaborates with scholars based in United States, Italy and United Kingdom. Thomas S. Deisboeck's co-authors include Zhihui Wang, Le Zhang, Yuri Mansury, Vittorio Cristini, E. Antonio Chiocca, M. Feldman, Chaitanya A. Athale, Griffith R. Harsh, Paul Macklin and Iain D. Couzin and has published in prestigious journals such as Nature Medicine, SHILAP Revista de lepidopterología and Applied Physics Letters.

In The Last Decade

Thomas S. Deisboeck

86 papers receiving 4.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas S. Deisboeck United States 35 2.0k 1.8k 1.1k 1.0k 601 88 4.4k
Jonathan A. Sherratt United Kingdom 48 2.3k 1.1× 1.4k 0.8× 717 0.7× 1.8k 1.8× 1.1k 1.7× 169 6.8k
Andreas Deutsch Germany 35 1.2k 0.6× 1.4k 0.8× 555 0.5× 1.1k 1.0× 297 0.5× 127 4.4k
Thomas Hillen Canada 34 3.7k 1.8× 1.9k 1.1× 620 0.6× 1.3k 1.3× 645 1.1× 108 5.2k
Natalia L. Komarova United States 42 1.3k 0.6× 2.2k 1.2× 1.1k 1.0× 637 0.6× 1.3k 2.2× 167 7.3k
Kristin R. Swanson United States 46 2.6k 1.3× 1.3k 0.7× 947 0.9× 695 0.7× 259 0.4× 136 7.3k
James A. Glazier United States 51 1.5k 0.7× 2.7k 1.5× 681 0.6× 2.8k 2.7× 331 0.6× 158 8.4k
Paul Macklin United States 24 1.7k 0.8× 1.0k 0.6× 868 0.8× 865 0.8× 110 0.2× 57 2.9k
Nicola Bellomo Italy 45 3.6k 1.7× 1.7k 0.9× 504 0.5× 806 0.8× 509 0.8× 244 7.4k
Markus R. Owen United Kingdom 32 992 0.5× 1.1k 0.6× 522 0.5× 439 0.4× 220 0.4× 80 3.2k
Dirk Drasdo Germany 33 1.3k 0.7× 1.1k 0.6× 749 0.7× 1.4k 1.4× 123 0.2× 61 3.3k

Countries citing papers authored by Thomas S. Deisboeck

Since Specialization
Citations

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

Fields of papers citing papers by Thomas S. Deisboeck

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas S. Deisboeck. 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 Thomas S. Deisboeck. The network helps show where Thomas S. Deisboeck may publish in the future.

Co-authorship network of co-authors of Thomas S. Deisboeck

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas S. Deisboeck. A scholar is included among the top collaborators of Thomas S. Deisboeck 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 Thomas S. Deisboeck. Thomas S. Deisboeck 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.
Lucia, Umberto, Giulia Grisolia, Antonio Ponzetto, & Thomas S. Deisboeck. (2024). Thermophysical Insights into the Anti-Inflammatory Potential of Magnetic Fields. Biomedicines. 12(11). 2534–2534.
2.
Lucia, Umberto, Thomas S. Deisboeck, Antonio Ponzetto, & Giulia Grisolia. (2023). A Thermodynamic Approach to the Metaboloepigenetics of Cancer. International Journal of Molecular Sciences. 24(4). 3337–3337. 5 indexed citations
3.
Lucia, Umberto, Debora Fino, Thomas S. Deisboeck, & Giulia Grisolia. (2023). A Thermodynamic Perspective of Cancer Cells’ Volume/Area Expansion Ratio. Membranes. 13(12). 895–895. 2 indexed citations
4.
Lucia, Umberto, Giulia Grisolia, & Thomas S. Deisboeck. (2021). A non-equilibrium thermodynamic approach to symmetry breaking in cancer. SHILAP Revista de lepidopterología. 1 indexed citations
5.
Lucia, Umberto, Antonio Ponzetto, & Thomas S. Deisboeck. (2016). Constructal approach to cell membranes transport: Amending the ‘Norton-Simon’ hypothesis for cancer treatment. Scientific Reports. 6(1). 19451–19451. 17 indexed citations
6.
Johnson, David, Steve McKeever, Thomas S. Deisboeck, & Zhihui Wang. (2013). Connecting digital cancer model repositories with markup. KTH Publication Database DiVA (KTH Royal Institute of Technology). 3(3). 5–11. 4 indexed citations
7.
Wang, Zhihui & Thomas S. Deisboeck. (2013). Mathematical modeling in cancer drug discovery. Drug Discovery Today. 19(2). 145–150. 52 indexed citations
8.
Wang, Zhihui, et al.. (2012). Accelerating cancer systems biology research through Semantic Web technology. WIREs Systems Biology and Medicine. 5(2). 135–151. 10 indexed citations
9.
10.
Wang, Zhiwei, et al.. (2010). Identifying therapeutic targets in a combined EGFR-TGF R signalling cascade using a multiscale agent-based cancer model. Mathematical Medicine and Biology A Journal of the IMA. 29(1). 95–108. 25 indexed citations
11.
Deisboeck, Thomas S., et al.. (2009). Investigating Differential Dynamics of the MAPK Signaling Cascade Using a Multi-Parametric Global Sensitivity Analysis. PLoS ONE. 4(2). e4560–e4560. 28 indexed citations
12.
Zhang, Le, Costas Strouthos, Zhihui Wang, & Thomas S. Deisboeck. (2008). Simulating brain tumor heterogeneity with a multiscale agent-based model: Linking molecular signatures, phenotypes and expansion rate. Mathematical and Computer Modelling. 49(1-2). 307–319. 59 indexed citations
13.
Sagotsky, Jonathan, et al.. (2008). Life Sciences and the web: a new era for collaboration. Molecular Systems Biology. 4(1). 201–201. 28 indexed citations
14.
Zhang, Le, Zhihui Wang, Jonathan Sagotsky, & Thomas S. Deisboeck. (2008). Multiscale agent-based cancer modeling. Journal of Mathematical Biology. 58(4-5). 545–559. 100 indexed citations
15.
Chen, L. Leon, et al.. (2008). Cancer cell motility: Optimizing spatial search strategies. Biosystems. 95(3). 234–242. 7 indexed citations
16.
Athale, Chaitanya A. & Thomas S. Deisboeck. (2005). The effects of EGF-receptor density on multiscale tumor growth patterns. Journal of Theoretical Biology. 238(4). 771–779. 63 indexed citations
17.
Mansury, Yuri, et al.. (2005). Evolutionary game theory in an agent-based brain tumor model: Exploring the ‘Genotype–Phenotype’ link. Journal of Theoretical Biology. 238(1). 146–156. 72 indexed citations
18.
Mansury, Yuri & Thomas S. Deisboeck. (2004). Simulating the time series of a selected gene expression profile in an agent-based tumor model. Physica D Nonlinear Phenomena. 196(1-2). 193–204. 16 indexed citations
19.
Mansury, Yuri & Thomas S. Deisboeck. (2003). The impact of “search precision” in an agent-based tumor model. Journal of Theoretical Biology. 224(3). 325–337. 51 indexed citations
20.
Habib, Salman, Carmen Molina-Parı́s, & Thomas S. Deisboeck. (2003). Complex dynamics of tumors: modeling an emerging brain tumor system with coupled reaction–diffusion equations. Physica A Statistical Mechanics and its Applications. 327(3-4). 501–524. 34 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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