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

2017-01-01
Ozmen, Ayse
Kropat, Erik
Weber, Gerhard Wilhelm
Show full item record
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
143
views
0
downloads
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.
Regulatory networks, Robust optimization, Polyhedral uncertainty, Conic quadratic programming, RCMARS
https://hdl.handle.net/11511/56777
Graduate School of Applied Mathematics, Article

Suggestions

ROBUST CONIC GENERALIZED PARTIAL LINEAR MODELS USING RCMARS METHOD - A ROBUSTIFICATION OF CGPLM
Ozmen, Ayse; Weber, Gerhard Wilhelm (2012-08-08)
GPLM is a combination of two different regression models each of which is used to apply on different parts of the data set. It is also adequate to high dimensional, non-normal and nonlinear data sets having the flexibility to reflect all anomalies effectively. In our previous study, Conic GPLM (CGPLM) was introduced using CMARS and Logistic Regression. According to a comparison with CMARS, CGPLM gives better results. In this study, we include the existence of uncertainty in the future scenarios into CMARS a...
OPTIMIZATION APPLIED ON REGULATORY AND ECO-FINANCE NETWORKS - SURVEY AND NEW DEVELOPMENTS -
Weber, Gerhard Wilhelm; Kropat, Erik; Tezel, Aysun; Belen, Selma (2010-05-01)
In this paper we survey recent advances and mathematical foundations of regulatory networks. We explain their interdisciplinary implications with special regard to Operational Research and financial sciences and introduce the so-called eco-finance networks. These networks, originally developed in the context of modeling and prediction of gene-expression patterns, have proved to provide a conceptual framework for the modeling of dynamical systems with respect to errors and uncertainty as well as the influenc...
Effective induction of gene regulatory networks using a novel recommendation method
Ozsoy, Makbule Gulcin; Polat, Faruk; Alhajj, Reda (2019-01-01)
In this paper, we introduce a method based on recommendation systems to predict the structure of Gene Regulatory Networks (GRNs) making use of data from multiple sources. Our method is based on collaborative filtering approach enhanced with multiple criteria to predict the relationships of genes, i.e., which genes regulate others. We conduct experiments on two data sets to demonstrate the applicability and sustainability of our proposal. The first data set is composed of microarray data and Transcription Fa...
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...
A new approach to multivariate adaptive regression splines by using Tikhonov regularization and continuous optimization
TAYLAN, PAKİZE; Weber, Gerhard Wilhelm; Ozkurt, Fatma Yerlikaya (2010-12-01)
This paper introduces a model-based approach to the important data mining tool Multivariate adaptive regression splines (MARS), which has originally been organized in a more model-free way. Indeed, MARS denotes a modern methodology from statistical learning which is important in both classification and regression, with an increasing number of applications in many areas of science, economy and technology. It is very useful for high-dimensional problems and shows a great promise for fitting nonlinear multivar...
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.
METU IIS - Integrated Information Service open@metu.edu.tr