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

Date

2017-01-01

Author

Ozmen, Ayse
Kropat, Erik
Weber, Gerhard Wilhelm

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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.

Subject Keywords

Regulatory networks, Robust optimization, Polyhedral uncertainty, Conic quadratic programming, RCMARS

URI

https://hdl.handle.net/11511/56777

Journal

OPTIMIZATION

DOI

https://doi.org/10.1080/02331934.2016.1209672

Collections

Graduate School of Applied Mathematics, Article

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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: https://hdl.handle.net/11511/56777.