DocumentCode :
399578
Title :
Adaptive modeling of biochemical pathways
Author :
Brause, R.
Author_Institution :
J.W.G. Univ., Frankfurt, Germany
fYear :
2003
fDate :
3-5 Nov. 2003
Firstpage :
62
Lastpage :
68
Abstract :
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.
Keywords :
biology computing; differential equations; evolutionary computation; learning (artificial intelligence); neural nets; adaptive modeling; biochemical pathways; bioinformatics; data; differential equation; inflammation modeling; learning algorithms; network; nonlinear dynamics evolutionary approach; Bioinformatics; Difference equations; Differential equations; Electric shock; Immune system; Neural networks; Nonlinear equations; Organisms; Pathogens; Predictive models;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE International Conference on
ISSN :
1082-3409
Print_ISBN :
0-7695-2038-3
Type :
conf
DOI :
10.1109/TAI.2003.1250171
Filename :
1250171
Link To Document :
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