title: Learning a Probabilistic Boolean Network model from biological pathways and time-series expression data.
abstract: The problem of inferring a stochastic model for gene regulatory networks is addressed here. The prior biological data includes biological pathways and time-series expression data. We propose a novel algorithm to use both of these data to construct a Probabilistic Boolean Network (PBN) which models the observed dynamics of genes with a high degree of precision. Our algorithm constructs a pathway tree and uses the time-series expression data to select an optimal level of tree, whose nodes are used to infer the PBN.
authors.forename: Vardaan
authors.initials: V
authors.lastname: Pahuja
authors.forename: Ritwik Kumar
authors.initials: RK
authors.lastname: Layek
authors.forename: Pabitra
authors.initials: P
authors.lastname: Mitra
mesh_terms: D000465:Algorithms; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D015233:Models, Statistical