title: Weighted-LASSO for structured network inference from time course data.
abstract: 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).
authors.affiliation: University of Evry-Val-d'Essonne. camille.charbonnier@genopole.cnrs.fr
authors.forename: Camille
authors.initials: C
authors.lastname: Charbonnier
authors.forename: Julien
authors.initials: J
authors.lastname: Chiquet
authors.forename: Christophe
authors.initials: C
authors.lastname: Ambroise
mesh_terms: D000465:Algorithms; D056808:Biostatistics; D002453:Cell Cycle / Q000235:genetics; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D053263:Gene Regulatory Networks; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D012044:Regression Analysis; D013014:SOS Response, Genetics / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics