Network estimation in State Space Models with L1-regularization constraint

Microarray technologies and related methods coupled with appropriate mathematical and statistical models have made it possible to identify dynamic regulatory networks by measuring time course expression levels of many genes simultaneously. However one of the challenges is the high-dimensional nature of such data coupled with the fact that these gene expression data are known not to include various biological process. As genomic interactions are highly structured, the aim was to derive a method for inferring a sparse dynamic network in a high dimensional data setting. The paper assumes that the observations are noisy measurements of gene expression in the form of mRNAs, whose dynamics can be described by some partially observed process. Key words : genomic; gene expression; microarray; sparse; EM algorithm; state space model.

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