Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks.

ABSTRACT: Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is 'stationarity', and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational effic

SUBMITTER: Zhu S 

PROVIDER: S-EPMC4683538 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature