A combined differential evolution and NSGA-II approach for parametric optimization of a cancer immunotherapy model

Weinan Xu; Jianxin Xu; Danhua He; Kay Chen Tan

doi:10.1109/SSCI.2017.8285259

A combined differential evolution and NSGA-II approach for parametric optimization of a cancer immunotherapy model

Weinan Xu, Jianxin Xu, Danhua He, Kay Chen Tan

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

2 Citations (Scopus)

Abstract

According to the clinical results from Manrique et. al. [1], a breast cancer immunotherapy model is established. The model is established based on biological principles and limited clinical results [1] for replications and prognostics of therapeutic effects. A single objective parametric optimization problem is formulated to find appropriate parameter values with biological meanings. Several constraints are formulated to satisfy both the disease progression without treatments and bio-system stability and equilibrium. To solve this parametric optimization problem with constraints automatically, the ϵ-DE+NSGA-II algorithm is proposed. The constraint violation is the second objective to be optimized in NSGA-II. The proposed ϵ-DE+NSGA-II can find the optimal parameters of the cancer immunotherapy model. The cancer immunotherapy model with the optimized parameters can not only replicate the clinical results from [1], but also provide prognostic outcomes for tumor rejection under various drug delivery schedules.

Original language	English
Title of host publication	2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-8
Number of pages	8
ISBN (Electronic)	9781538627259
DOIs	https://doi.org/10.1109/SSCI.2017.8285259
Publication status	Published - 2 Feb 2018
Externally published	Yes
Event	2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States Duration: 27 Nov 2017 → 1 Dec 2017

Publication series

Name	2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Volume	2018-January

Conference

Conference	2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Country/Territory	United States
City	Honolulu
Period	27/11/17 → 1/12/17

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Control and Optimization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

More information

10.1109/SSCI.2017.8285259

Cite this

Xu, W., Xu, J., He, D., & Tan, K. C. (2018). A combined differential evolution and NSGA-II approach for parametric optimization of a cancer immunotherapy model. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (pp. 1-8). (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2017.8285259

Xu, Weinan ; Xu, Jianxin ; He, Danhua et al. / A combined differential evolution and NSGA-II approach for parametric optimization of a cancer immunotherapy model. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-8 (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings).

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abstract = "According to the clinical results from Manrique et. al. [1], a breast cancer immunotherapy model is established. The model is established based on biological principles and limited clinical results [1] for replications and prognostics of therapeutic effects. A single objective parametric optimization problem is formulated to find appropriate parameter values with biological meanings. Several constraints are formulated to satisfy both the disease progression without treatments and bio-system stability and equilibrium. To solve this parametric optimization problem with constraints automatically, the ϵ-DE+NSGA-II algorithm is proposed. The constraint violation is the second objective to be optimized in NSGA-II. The proposed ϵ-DE+NSGA-II can find the optimal parameters of the cancer immunotherapy model. The cancer immunotherapy model with the optimized parameters can not only replicate the clinical results from [1], but also provide prognostic outcomes for tumor rejection under various drug delivery schedules.",

author = "Weinan Xu and Jianxin Xu and Danhua He and Tan, {Kay Chen}",

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Xu, W, Xu, J, He, D & Tan, KC 2018, A combined differential evolution and NSGA-II approach for parametric optimization of a cancer immunotherapy model. in 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-8, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, United States, 27/11/17. https://doi.org/10.1109/SSCI.2017.8285259

A combined differential evolution and NSGA-II approach for parametric optimization of a cancer immunotherapy model. / Xu, Weinan; Xu, Jianxin; He, Danhua et al.
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-8 (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings; Vol. 2018-January).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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Xu W, Xu J, He D, Tan KC. A combined differential evolution and NSGA-II approach for parametric optimization of a cancer immunotherapy model. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-8. (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings). doi: 10.1109/SSCI.2017.8285259

A combined differential evolution and NSGA-II approach for parametric optimization of a cancer immunotherapy model

Abstract

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ASJC Scopus subject areas

UN SDGs

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