Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty

In our study, we integrate the data uncertainty of real-world models into our regulatory systems and robustify them. We newly introduce and analyse robust time-discrete target-environment regulatory systems under polyhedral uncertainty through robust optimization. Robust optimization has reached a g...

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Bibliographic Details
Published inOptimization Vol. 66; no. 12; pp. 2135 - 2155
Main Authors Özmen, Ayşe, Kropat, Erik, Weber, Gerhard-Wilhelm
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.12.2017
Taylor & Francis LLC
Subjects
conic quadratic programming
Immunization
Nonlinear programming
Optimization
polyhedral uncertainty
RCMARS
Regression analysis
Regression models
Regulatory networks
robust optimization
Robustness (mathematics)
Uncertainty
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Summary:In our study, we integrate the data uncertainty of real-world models into our regulatory systems and robustify them. We newly introduce and analyse robust time-discrete target-environment regulatory systems under polyhedral uncertainty through robust optimization. Robust optimization has reached a great importance as a modelling framework for immunizing against parametric uncertainties and the integration of uncertain data is of considerable importance for the model's reliability of a highly interconnected system. Then, we present a numerical example to demonstrate the efficiency of our new robust regression method for regulatory networks. The results indicate that our approach can successfully approximate the target-environment interaction, based on the expression values of all targets and environmental factors.
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ISSN:0233-1934
1029-4945
DOI:10.1080/02331934.2016.1209672