Browsing by Author "Weber, Gerhard-Wilhelm"

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Citation - WoS: 49
Citation - Scopus: 51
Fuzzy prediction strategies for gene-environment networks - fuzzy regression analysis for two-modal regulatory systems
(Edp Sciences S A, 2016) Kropat, Erik; Defterli, Özlem; Ozmen, Ayse; Weber, Gerhard-Wilhelm; Meyer-Nieberg, Silja; Defterli, Ozlem; 31401; Matematik
Target-environment networks provide a conceptual framework for the analysis and prediction of complex regulatory systems such as genetic networks, eco-finance networks or sensor-target assignments. These evolving networks consist of two major groups of entities that are interacting by unknown relationships. The structure and dynamics of the hidden regulatory system have to be revealed from uncertain measurement data. In this paper, the concept of fuzzy target-environment networks is introduced and various fuzzy possibilistic regression models are presented. The relation between the targets and/or environmental entities of the regulatory network is given in terms of a fuzzy model. The vagueness of the regulatory system results from the (unknown) fuzzy coefficients. For an identification of the fuzzy coefficients' shape, methods from fuzzy regression are adapted and made applicable to the bi-level situation of target-environment networks and uncertain data. Various shapes of fuzzy coefficients are considered and the control of outliers is discussed. A first numerical example is presented for purposes of illustration. The paper ends with a conclusion and an outlook to future studies.
Citation - WoS: 50
Citation - Scopus: 58
Modeling, inference and optimization of regulatory networks based on time series data
(Elsevier, 2011) Weber, Gerhard-Wilhelm; Defterli, Özlem; Defterli, Ozlem; Gok, Sirma Zeynep Alparslan; Kropat, Erik; ; 31401; 107899; Matematik
In this survey paper, we present advances achieved during the last years in the development and use of OR, in particular, optimization methods in the new gene-environment and eco-finance networks, based on usually finite data series, with an emphasis on uncertainty in them and in the interactions of the model items. Indeed, our networks represent models in the form of time-continuous and time-discrete dynamics, whose unknown parameters we estimate under constraints on complexity and regularization by various kinds of optimization techniques, ranging from linear, mixed-integer, spline, semi-infinite and robust optimization to conic, e.g., semi-definite programming. We present different kinds of uncertainties and a new time-discretization technique, address aspects of data preprocessing and of stability, related aspects from game theory and financial mathematics, we work out structural frontiers and discuss chances for future research and OR application in our real world. (C) 2010 Elsevier B.V. All rights reserved.
Citation - WoS: 30
Citation - Scopus: 35
The new robust conic GPLM method with an application to finance: prediction of credit default
(Springer, 2013) Ozmen, Ayse; Defterli, Özlem; Weber, Gerhard-Wilhelm; Cavusoglu, Zehra; Defterli, Ozlem; 31401; Matematik
This paper contributes to classification and identification in modern finance through advanced optimization. In the last few decades, financial misalignments and, thereby, financial crises have been increasing in numbers due to the rearrangement of the financial world. In this study, as one of the most remarkable of these, countries' debt crises, which result from illiquidity, are tried to predict with some macroeconomic variables. The methodology consists of a combination of two predictive regression models, logistic regression and robust conic multivariate adaptive regression splines (RCMARS), as linear and nonlinear parts of a generalized partial linear model. RCMARS has an advantage of coping with the noise in both input and output data and of obtaining more consistent optimization results than CMARS. An advanced version of conic generalized partial linear model which includes robustification of the data set is introduced: robust conic generalized partial linear model (RCGPLM). This new model is applied on a data set that belongs to 45 emerging markets with 1,019 observations between the years 1980 and 2005.
Citation - WoS: 2
Citation - Scopus: 4
Vester's Sensitivity Model for Genetic Networks With Time-Discrete Dynamics
(Springer international Publishing Ag, 2014) Moreno, Liana Amaya; Defterli, Ozlem; Fuegenschuh, Armin; Weber, Gerhard-Wilhelm
We propose a new method to explore the characteristics of genetic networks whose dynamics are described by a linear discrete dynamical model x(t+1) = Ax(t). The gene expression data x(t) is given for various time points and the matrix A of interactions among the genes is unknown. First we formulate and solve a parameter estimation problem by linear programming in order to obtain the entries of the matrix A. We then use ideas from Vester's Sensitivity Model, more precisely, the Impact Matrix, and the determination of the Systemic Roles, to understand the interactions among the genes and their role in the system. The method identifies prominent outliers, that is, the most active, reactive, buffering and critical genes in the network. Numerical examples for different datasets containing mRNA transcript levels during the cell cycle of budding yeast are presented.