Bayesian network reconstruction using systems genetics data: comparison of MCMC methods.

ABSTRACT: Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis-Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from kn

SUBMITTER: Tasaki S 

PROVIDER: S-EPMC4391572 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature