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Experimental Design for Efficient Identification of Gene Regulatory Networks using Sparse Bayesian Models

Experimental Design for Efficient Identification of Gene Regulatory Networks using Sparse Bayesian Models

This video was recorded at Workshop on Parameter Estimation in Systems Biology, Manchester 2007. Identifying large gene regulatory networks is an important task, while the acquisition of data through perturbation experiments (e.g., gene switches, RNAi) 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. 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... Show More

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