Nonparametric identification of regulatory interactions from spatial and temporal gene expression data.

ABSTRACT:

Background

The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions.

Results

Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting. It generates spatio-temporal maps of factor activity, highlighting the times and spatial locations at which diffe

SUBMITTER: Aswani A 

PROVIDER: S-EPMC2933715 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature