Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network.

ABSTRACT:

Background

Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks.

Results

To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a p

SUBMITTER: Xuan NV 

PROVIDER: S-EPMC3433362 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature