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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Notes
- 1.
Note that this observation is related to the number of observations that is possible to have in currently available clinical studies, rather than to the method we are applying.
- 2.
The identification of a tree composed of a single element is a case that is strongly influenced by noise.
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Acknowledgments
The work of Laura Sani was supported by a grant from Regione Emilia Romagna (“Creazione di valore per imprese e società con la gestione e l’analisi di BIG DATA”, POR FSE 2014/2020, Obiettivo Tematico 10) within the “Piano triennale alte competenze per la ricerca, il trasferimento tecnologico e l’imprenditorialità” framework, and by Infor srl.
Marco Villani thanks the support provided by the FAR2019 project of the Department of Physics, Informatics and Mathematics of the University of Modena and Reggio Emilia.
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Sani, L., D’Addese, G., Graudenzi, A., Villani, M. (2020). The Detection of Dynamical Organization in Cancer Evolution Models. In: Cicirelli, F., Guerrieri, A., Pizzuti, C., Socievole, A., Spezzano, G., Vinci, A. (eds) Artificial Life and Evolutionary Computation. WIVACE 2019. Communications in Computer and Information Science, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-45016-8_6
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