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Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks
2006
Signal Processing
A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and
doi:10.1016/j.sigpro.2005.06.008
pmid:17415411
pmcid:PMC1847796
fatcat:r2tcldd4rnbddimmfentvpfirm