Learning a Probabilistic Boolean Network model from biological pathways and time-series expression data

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Vardaan PahujaPabitra Mitra


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.

Related Concepts

Biochemical Pathway
Trees (plant)
Gene Regulatory Networks
Two-Parameter Models
Anatomic Node
Gene Modules
Protein Expression

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