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Modeling breast tumor growth by a randomized logistic model: A computational approach to treat uncertainties via probability densities

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Modeling breast tumor growth by a randomized logistic model: A computational approach to treat uncertainties via probability densities

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Nombre: EPJP_CANCER_2020.pdf
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dc.contributor.author Burgos-Simón, Clara es_ES
dc.contributor.author Cortés, J.-C. es_ES
dc.contributor.author Martínez-Rodríguez, David es_ES
dc.contributor.author Villanueva Micó, Rafael Jacinto es_ES
dc.date.accessioned 2021-02-11T04:32:13Z
dc.date.available 2021-02-11T04:32:13Z
dc.date.issued 2020-10-14 es_ES
dc.identifier.uri http://hdl.handle.net/10251/161047
dc.description.abstract [EN] We consider a randomized discrete logistic equation to describe the dynamics of breast tumor volume. We propose a method, that takes advantage of the principle of maximum entropy, to assign reliable distributions to model inputs (initial condition and coefficients) and sample data, respectively. Since the distributions of coefficients depend on certain parameters, we design a computational procedure to determine the above mentioned parameters using the information of the probabilistic distributions. The proposed method is successfully applied to model the breast tumor volume using real data. The approach seems to be flexible enough to be adapted to other stochastic models in future contributions. es_ES
dc.description.sponsorship This work has been supported by the Spanish Ministerio de Economia, Industria y Competitividad (MINECO), the Agencia Estatal de Investigacion (AEI), and Fondo Europeo de Desarrollo Regional (FEDER UE) Grants MTM2017-89664-P and RTI2018-095180-B-I00. es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof European Physical Journal Plus es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Maximum entropy principle es_ES
dc.subject Computational model fitting es_ES
dc.subject Volume tumor growth es_ES
dc.subject Uncertainty treatment es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Modeling breast tumor growth by a randomized logistic model: A computational approach to treat uncertainties via probability densities es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1140/epjp/s13360-020-00853-3 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095180-B-I00/ES/SISTEMA ADAPTATIVO BIOINSPIRADO PARA EL CONTROL GLUCEMICO BASADO EN SENSORES Y ACCESORIOS INTELIGENTES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-89664-P/ES/PROBLEMAS DINAMICOS CON INCERTIDUMBRE SIMULABLE: MODELIZACION MATEMATICA, ANALISIS, COMPUTACION Y APLICACIONES/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Matemática Multidisciplinar - Institut Universitari de Matemàtica Multidisciplinària es_ES
dc.description.bibliographicCitation Burgos-Simón, C.; Cortés, J.; Martínez-Rodríguez, D.; Villanueva Micó, RJ. (2020). Modeling breast tumor growth by a randomized logistic model: A computational approach to treat uncertainties via probability densities. European Physical Journal Plus. 135(10):1-14. https://doi.org/10.1140/epjp/s13360-020-00853-3 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://www.doi.org/10.1140/epjp/s13360-020-00853-3 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 14 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 135 es_ES
dc.description.issue 10 es_ES
dc.identifier.eissn 2190-5444 es_ES
dc.relation.pasarela S\419645 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES
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