Szczegóły publikacji

Opis bibliograficzny

Supermodeling in predictive diagnostics of cancer under treatment / Witold DZWINEL, Adrian KŁUSEK, Leszek SIWIK // Computers in Biology and Medicine ; ISSN 0010-4825. — 2021 — vol. 137 art. no. 104797, s. 1–16. — Bibliogr. s. 15–16, Abstr. — Publikacja dostępna online od: 2021-08-28


Autorzy (3)


Słowa kluczowe

efficient tumor modeldata assimilationcancer predictive systemanticancer therapysupermodeling

Dane bibliometryczne

ID BaDAP136006
Data dodania do BaDAP2021-10-11
Tekst źródłowyURL
DOI10.1016/j.compbiomed.2021.104797
Rok publikacji2021
Typ publikacjiartykuł w czasopiśmie
Otwarty dostęptak
Creative Commons
Czasopismo/seriaComputers in Biology and Medicine

Abstract

Classical data assimilation (DA) techniques, synchronizing a computer model with observations, are highly demanding computationally, particularly, for complex over-parametrized cancer models. Consequently, current models are not sufficiently flexible to interactively explore various therapy strategies, and to become a key tool of predictive oncology. We show that, by using supermodeling, it is possible to develop a prediction/correction scheme that could attain the required time regimes and be directly used to support decision-making in anticancer therapies. A supermodel is an interconnected ensemble of individual models (sub-models); in this case, the variously parametrized baseline tumor models. The sub-model connection weights are trained from data, thereby incorporating the advantages of the individual models. Simultaneously, by optimizing the strengths of the connections, the sub-models tend to partially synchronize with one another. As a result, during the evolution of the supermodel, the systematic errors of the individual models partially cancel each other. We find that supermodeling allows for a radical increase in the accuracy and efficiency of data assimilation. We demonstrate that it can be considered as a meta-procedure for any classical parameter fitting algorithm, thus it represents the next – latent – level of abstraction of data assimilation. We conclude that supermodeling is a very promising paradigm that can considerably increase the quality of prognosis in predictive oncology.

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