Weighted-Lasso for Structured Network Inference from Time Course Data

Details Weighted-Lasso for Structured Network Inference from Time Course Data Weighted-Lasso for Structured Network Inference from Time Course Data

2009-10-09

arxiv.org/abs/0910.1723v2

Statistical Applications in Genetics and Molecular Biology: Vol. 9 : Iss. 1, Article 15, 2010.

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Scientific paper

10.2202/1544-6115.1519

We present a weighted-Lasso method to infer the parameters of a first-order vector auto-regressive model that describes time course expression data generated by directed gene-to-gene regulation networks. These networks are assumed to own a prior internal structure of connectivity which drives the inference method. This prior structure can be either derived from prior biological knowledge or inferred by the method itself. We illustrate the performance of this structure-based penalization both on synthetic data and on two canonical regulatory networks, first yeast cell cycle regulation network by analyzing Spellman et al's dataset and second E. coli S.O.S. DNA repair network by analysing U. Alon's lab data.

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