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Memetic Computing

Erschienen in:

30.10.2020 | Regular Research Paper

Designing optimal combination therapy for personalised glioma treatment

verfasst von: Nasimul Noman, Pablo Moscato

Erschienen in: Memetic Computing | Ausgabe 4/2020

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Abstract

Background

Like it happens in other tumours, glioma cells co-evolve in a microenvironment consisting of bona fide tumour cells as well as a range of parenchymal cells, which produces numerous signalling molecules. Recently, the results of an in silico experiment suggested that a combination therapy that would target multiple key cytokines at the same time may be more effective for suppressing the growth of a tumour. The in silico experiments also showed that the optimal combination therapy is very much dependent on a patient’s molecular profile.

Method

In this work, we employ evolutionary algorithms for designing optimal combination therapy tailored to the patient’s tumour microenvironment. Experiments were performed using a state-of-the-art glioma microenvironment model, capable of imitating many characteristics of human glioma development, and many virtual patient profiles.

Conclusions

Results show that the therapies designed by the presented memetic algorithm were very effective in impeding tumour growth and were tailored to the patient’s personal tumour microenvironment.

Vorheriger Artikel An efficient memetic genetic programming framework for symbolic regression

Nächster Artikel Cooperative co-evolutionary comprehensive learning particle swarm optimizer for formulation design of explosive simulant

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The basic ABC version available from https://abc.erciyes.edu.tr/software.htm was used.

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Titel: Designing optimal combination therapy for personalised glioma treatment
verfasst von: Nasimul Noman
Pablo Moscato
Publikationsdatum: 30.10.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Memetic Computing / Ausgabe 4/2020
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI: https://doi.org/10.1007/s12293-020-00312-7

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Abstract

Background

Method

Conclusions

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