Function approximation approach to the inference of reduced NGnet models of genetic networks.

ABSTRACT:

Background

The inference of a genetic network is a problem in which mutual interactions among genes are deduced using time-series of gene expression patterns. While a number of models have been proposed to describe genetic regulatory networks, this study focuses on a set of differential equations since it has the ability to model dynamic behavior of gene expression. When we use a set of differential equations to describe genetic networks, the inference problem can be defined as a function approximation problem. On the basis of this problem definition, we propose in this study a new method to infer reduced NGnet models of genetic networks.

Results

Through numerical experiments on artificial genetic network inference problems, we demonstrated that our method has the ability to

SUBMITTER: Kimura S 

PROVIDER: S-EPMC2258286 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature