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
Dane bibliometryczne
| ID BaDAP | 136006 |
|---|---|
| Data dodania do BaDAP | 2021-10-11 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.compbiomed.2021.104797 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Computers 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.
