Many systems in nature, society and technology are composed of numerous interacting parts. Very often these dynamics lead to the formation of medium-level structures, whose detection could allow a high-level description of the dynamical organization of the system itself, and thus to its understanding. In this work we apply this idea to the “cancer evolution” models, of which each individual patient represents a particular instance. This approach - in this paper based on the RI methodology, which is based on entropic measures - allows us to identify distinct independent cancer progression patterns in simulated patients, planning a road towards applications to real cases.
Sani, L., D'Addese, G., Graudenzi, A., Villani, M. (2020). The detection of dynamical organization in cancer evolution models. In Artificial Life and Evolutionary Computation. 14th Italian Workshop, WIVACE 2019, Rende, Italy, September 18–20, 2019, Revised Selected Papers (pp.49-61). Springer [10.1007/978-3-030-45016-8_6].
The detection of dynamical organization in cancer evolution models
Graudenzi, A;
2020
Abstract
Many systems in nature, society and technology are composed of numerous interacting parts. Very often these dynamics lead to the formation of medium-level structures, whose detection could allow a high-level description of the dynamical organization of the system itself, and thus to its understanding. In this work we apply this idea to the “cancer evolution” models, of which each individual patient represents a particular instance. This approach - in this paper based on the RI methodology, which is based on entropic measures - allows us to identify distinct independent cancer progression patterns in simulated patients, planning a road towards applications to real cases.
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