Dataset Information

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 polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques.

Conclusions

Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks.

SUBMITTER: Xuan NV

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

REPOSITORIES: biostudies-literature

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Publications

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

Xuan Nguyen Vinh NV Chetty Madhu M Chetty Madhu M Coppel Ross R Wangikar Pramod P PP

BMC bioinformatics 20120613

<h4>Background</h4>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 network ...[more]

PMID: 22694481

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Dataset Information

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

Background

Results

Conclusions

Publications

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

Similar Datasets

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