Adaptive modeling of biochemical pathways

  • In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data. Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2003

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Metadaten
Author:Rüdiger W. BrauseGND
URN:urn:nbn:de:hebis:30-79219
Parent Title (German):Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2003, IEEE Press 2003
Document Type:Article
Language:English
Date of Publication (online):2010/09/08
Year of first Publication:2003
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2010/09/08
First Page:62
Last Page:68
Source:Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2003, IEEE Press 2003, pp.62-68, (2003), "Best paper" award of ICTAI-2003
HeBIS-PPN:227976614
Institutes:Informatik und Mathematik / Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Sammlungen:Universitätspublikationen
Licence (German):License LogoDeutsches Urheberrecht