Geodesic
Fuzzy prediction strategies for gene-environment networks – Fuzzy regression analysis for two-modal regulatory systems
Kropat, Erik1 ; Özmen, Ayşe2 ; Weber, Gerhard-Wilhelm2 ; Meyer-Nieberg, Silja3 ; Defterli, Ozlem4
1 Institute for Applied Computer Science, Universität der Bundeswehr München, 85577 Neubiberg, Germany.
2 Institute of Applied Mathematics, Middle East Technical University, 06531 Ankara, Turkey.
3 Institute for Theoretical Computer Science, Mathematics and Operations Research, Universität der Bundeswehr München, 85577 Neubiberg, Germany.
4 Faculty of Arts and Sciences, Department of Mathematics and Computer Science,Çankaya University, 06810 Ankara, Turkey.
RAIRO - Operations Research - Recherche Opérationnelle, Special issue: Research on Optimization and Graph Theory dedicated to COSI 2013 / Special issue: Recent Advances in Operations Research in Computational Biology, Bioinformatics and Medicine, Tome 50 (2016) no. 2, pp. 413-435

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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.

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DOI : 10.1051/ro/2015044
Classification : 92-08, 92D10, 62J86
Keywords: Fuzzy evolving networks, fuzzy target-environment networks, uncertainty, fuzzy theory, fuzzy regression analysis, possibilistic regression, forecasting

Kropat, Erik 1 ; Özmen, Ayşe 2 ; Weber, Gerhard-Wilhelm 2 ; Meyer-Nieberg, Silja 3 ; Defterli, Ozlem 4

1 Institute for Applied Computer Science, Universität der Bundeswehr München, 85577 Neubiberg, Germany.
2 Institute of Applied Mathematics, Middle East Technical University, 06531 Ankara, Turkey.
3 Institute for Theoretical Computer Science, Mathematics and Operations Research, Universität der Bundeswehr München, 85577 Neubiberg, Germany.
4 Faculty of Arts and Sciences, Department of Mathematics and Computer Science,Çankaya University, 06810 Ankara, Turkey. 
@article{RO_2016__50_2_413_0,
     author = {Kropat, Erik and \"Ozmen, Ay\c{s}e and Weber, Gerhard-Wilhelm and Meyer-Nieberg, Silja and Defterli, Ozlem},
     title = {Fuzzy prediction strategies for gene-environment networks {\textendash} {Fuzzy} regression analysis for two-modal regulatory systems},
     journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
     pages = {413--435},
     publisher = {EDP-Sciences},
     volume = {50},
     number = {2},
     year = {2016},
     doi = {10.1051/ro/2015044},
     zbl = {1357.92032},
     mrnumber = {3479880},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.1051/ro/2015044/}
}
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Kropat, Erik; Özmen, Ayşe; Weber, Gerhard-Wilhelm; Meyer-Nieberg, Silja; Defterli, Ozlem. Fuzzy prediction strategies for gene-environment networks – Fuzzy regression analysis for two-modal regulatory systems. RAIRO - Operations Research - Recherche Opérationnelle, Special issue: Research on Optimization and Graph Theory dedicated to COSI 2013 / Special issue: Recent Advances in Operations Research in Computational Biology, Bioinformatics and Medicine, Tome 50 (2016) no. 2, pp. 413-435. doi: 10.1051/ro/2015044

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