Fuzzy prediction strategies for gene-environment networks – Fuzzy regression analysis for two-modal regulatory systems

Abstract

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.

Fuzzy target-environment networks and fuzzy-regression approaches

“…In this research paper, we set a particular focus on possibilistic regression [31]. We further extend the regression approaches presented in [13] in order to include fuzzy input data or a fuzzification of crisp measurements into our modeling. In some situations, the unknown interactions of the entities of the system have to be revealed from measurements and observations of the environmental factors.…”

“…Then, in Section 3, the corresponding crisp data sets and fuzzy data sets as well as appropriate inclusion relations are presented. Section 4 briefly reviews some fuzzy-regression approaches from [13]. Here, fuzzy-regression models based on symmetric and asymmetric triangular fuzzy coefficients as well as trapezoidal fuzzy coefficients are discussed.…”

Recent Advances in Operations Research in Computational Biology, Bioinformatics and Medicine

“…Kropat et al [6] presents the concept of fuzzy target-environment networks together with various fuzzy possibilistic regression models, for the analysis of two-modal regulatory systems affected by errors and uncertainty. The analyzed fuzzy regression approaches are very flexible and can be adapted to a variety of regulatory systems where data uncertainty and model restrictions are involved.…”

An approximate optimization strategy using refined hybrid metamodel

“…In addition, there is a regression technique called multivariate adaptive regression spline in the field of statistics, which is a nonparametric regression technique that constructs multiple linear regression models across the range of independent variables . When the variables have uncertainties, we can predict the function relation between independent and dependent variables through fuzzy regression analysis …”

“…7,8 When the variables have uncertainties, we can predict the function relation between independent and dependent variables through fuzzy regression analysis. 9 Each of these metamodel techniques has its advantages and disadvantages. Recently, methods for generating hybrid models by combining these metamodels have been developed.…”