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

Ozmen, Ayse
Kropat, Erik
Weber, Gerhard Wilhelm
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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Robust conic quadratic programming applied to quality improvement -a robustification of CMARS
Özmen, Ayşe; Weber, Gerhard Wilhelm; Batmaz, İnci; Department of Scientific Computing (2010)
In this thesis, we study and use Conic Quadratic Programming (CQP) for purposes of operational research, especially, for quality improvement in manufacturing. In previous works, the importance and benefit of CQP in this area became already demonstrated. There, the complexity of the regression method Multivariate Adaptive Regression Spline (MARS), which especially means sensitivity with respect to noise in the data, became penalized in the form of so-called Tikhonov regularization, which became expressed and...
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Citation Formats
A. Ozmen, E. Kropat, and G. W. Weber, “Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty,” OPTIMIZATION, pp. 2135–2155, 2017, Accessed: 00, 2020. [Online]. Available: