Weighted-LASSO for Structured Network Inference from Time Course Data

Camille Charbonnier 1 , Julien Chiquet 2  and Christophe Ambroise 3
  • 1 University of Évry-Val-d’Essonne
  • 2 University of Évry-Val-d’Essonne
  • 3 University of Évry-Val-d’Essonne

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 prior internal structures of connectivity which drive 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 (the yeast cell cycle regulation network and the E. coli S.O.S. DNA repair network).

    • supp_material.pdf
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SAGMB publishes significant research on the application of statistical ideas to problems arising from computational biology. The range of topics includes linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarrary data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies.

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