Dynamical gene-environment networks under ellipsoidal uncertainty: set-theoretic regression analysis based on ellipsoidal or

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dc.authoridWeber, Gerhard-Wilhelm -- 0000-0003-0849-7771; Kropat, Erik -- 0000-0002-0551-9747
dc.contributor.authorKropat, Erik
dc.contributor.authorWeber, Gerhard-Wilhelm
dc.contributor.authorBelen, Selma
dc.contributor.editorPeixoto, MM
dc.contributor.editorPinto, AA
dc.contributor.editorRand, DA
dc.date.accessioned12.07.201910:50:10
dc.date.accessioned2019-07-12T15:28:08Z
dc.date.available12.07.201910:50:10
dc.date.available2019-07-12T15:28:08Z
dc.date.issued2011
dc.departmentFen Edebiyat Fakültesien_US
dc.descriptionInternational Conference on Dynamical Systems and Game Theory in Honor of Mauricio Peixoto and David Rand -- SEP 08-12, 2008 -- Univ Minho, Braga, PORTUGALen_US
dc.descriptionWOS: 000393993000035en_US
dc.description.abstractWe consider dynamical gene-environment networks under ellipsoidal uncertainty and discuss the corresponding set-theoretic regression models. Clustering techniques are applied for an identification of functionally related groups of genes and environmental factors. Clusters can partially overlap as single genes possibly regulate multiple groups of data items. The uncertain states of cluster elements are represented in terms of ellipsoids referring to stochastic dependencies between the multivariate data variables. The time-dependent behaviour of the system variables and clusters is determined by a regulatory system with (affine-) linear coupling rules. Explicit representations of the uncertain multivariate future states of the system are calculated by ellipsoidal calculus. Various set-theoretic regression models are introduced in order to estimate the unknown system parameters. Hereby, we extend our Ellipsoidal Operations Research previously introduced for gene-environment networks of strictly disjoint clusters to possibly overlapping clusters. We analyze the corresponding optimization problems, in particular in view of their solvability by interior point methods and semidefinite programming and we conclude with a discussion of structural frontiers and future research challenges.en_US
dc.identifier.doi10.1007/978-3-642-11456-4_35
dc.identifier.endpage571en_US
dc.identifier.isbn978-3-642-11456-4; 978-3-642-11455-7
dc.identifier.issn2190-5614
dc.identifier.scopus2-s2.0-84930168029
dc.identifier.scopusqualityN/A
dc.identifier.startpage545en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-642-11456-4_35
dc.identifier.urihttps://hdl.handle.net/20.500.12507/639
dc.identifier.volume1en_US
dc.identifier.wosWOS:000393993000035
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSprınger-Verlag Berlınen_US
dc.relation.ispartofDYNAMICS, GAMES AND SCIENCE I
dc.relation.ispartofseriesSpringer Proceedings in Mathematics
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleDynamical gene-environment networks under ellipsoidal uncertainty: set-theoretic regression analysis based on ellipsoidal or
dc.typeConference Object

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