Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty. (2nd December 2017)

• Record Type: Journal Article
• Authors: Özmen, Ayşe; Kropat, Erik; Weber, Gerhard-Wilhelm
• Is Part Of: Optimization. Volume 66:Number 12(2017)
• Page Start: 2135
• Page End: 2155
• Publication Date: 2017-12-02
• Subjects: Regulatory networks -- robust optimization -- polyhedral uncertainty -- conic quadratic programming -- RCMARS; 80M50 -- 90B10 -- 90C30 -- 90C55 -- 90C90; Mathematical optimization -- Periodicals; 519.7
• DOI: 10.1080/02331934.2016.1209672
• Languages: English
• ISSNs: 0233-1934

Abstract

Abstract: 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.