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• W2492654920 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." @default.
• W2492654920 created "2016-08-23" @default.
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• W2492654920 date "2016-07-22" @default.
• W2492654920 modified "2023-10-18" @default.
• W2492654920 title "Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty" @default.
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• W2492654920 doi "https://doi.org/10.1080/02331934.2016.1209672" @default.
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