Title :
Adaptive modeling of biochemical pathways
Author_Institution :
J.W.G. Univ., Frankfurt, Germany
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;
Conference_Titel :
Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE International Conference on
Print_ISBN :
0-7695-2038-3
DOI :
10.1109/TAI.2003.1250171