State Space Model with hidden variables for reconstruction of gene regulatory networksReport as inadecuate

State Space Model with hidden variables for reconstruction of gene regulatory networks - Download this document for free, or read online. Document in PDF available to download.

BMC Systems Biology

, 5:S3

First Online: 23 December 2011


BackgroundState Space Model SSM is a relatively new approach to inferring gene regulatory networks. It requires less computational time than Dynamic Bayesian Networks DBN. There are two types of variables in the linear SSM, observed variables and hidden variables. SSM uses an iterative method, namely Expectation-Maximization, to infer regulatory relationships from microarray datasets. The hidden variables cannot be directly observed from experiments. How to determine the number of hidden variables has a significant impact on the accuracy of network inference. In this study, we used SSM to infer Gene regulatory networks GRNs from synthetic time series datasets, investigated Bayesian Information Criterion BIC and Principle Component Analysis PCA approaches to determining the number of hidden variables in SSM, and evaluated the performance of SSM in comparison with DBN.

MethodTrue GRNs and synthetic gene expression datasets were generated using GeneNetWeaver. Both DBN and linear SSM were used to infer GRNs from the synthetic datasets. The inferred networks were compared with the true networks.

ResultsOur results show that inference precision varied with the number of hidden variables. For some regulatory networks, the inference precision of DBN was higher but SSM performed better in other cases. Although the overall performance of the two approaches is compatible, SSM is much faster and capable of inferring much larger networks than DBN.

ConclusionThis study provides useful information in handling the hidden variables and improving the inference precision.

List of abbreviations usedSSMState Space Model

DBNDynamic Bayesian Networks

GRNsGene regulatory networks

BICBayesian Information Criterion

PCAPrinciple Component Analysis

PBNProbability Boolean Network.

Download fulltext PDF

Author: Xi Wu - Peng Li - Nan Wang - Ping Gong - Edward J Perkins - Youping Deng - Chaoyang Zhang


Related documents