Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models.

ABSTRACT:

Background

Identifying large gene regulatory networks is an important task, while the acquisition of data through perturbation experiments (e.g., gene switches, RNAi, heterozygotes) is expensive. It is thus desirable to use an identification method that effectively incorporates available prior knowledge - such as sparse connectivity - and that allows to design experiments such that maximal information is gained from each one.

Results

Our main contributions are twofold: a method for consistent inference of network structure is provided, incorporating prior knowledge about sparse connectivity. The algorithm is time efficient and robust to violations of model assumptions. Moreover, we show how to use it for optimal experimental design, reducing the number of required experiment

SUBMITTER: Steinke F 

PROVIDER: S-EPMC2233642 | biostudies-literature | 2007 Nov

REPOSITORIES: biostudies-literature