Open AccessGaussian graphical model for identifying significantly responsive regulatory networks from time course high‐throughput data
Author(s) -
Liu ZhiPing,
Zhang Wanwei,
Horimoto Katsuhisa,
Chen Luonan
Publication year - 2013
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/iet-syb.2012.0062
Subject(s) - gene regulatory network , graphical model , computer science , dynamic bayesian network , bayesian network , biological network , consistency (knowledge bases) , computational biology , gene expression profiling , gaussian , systems biology , data mining , machine learning , artificial intelligence , gene , gene expression , biology , genetics , physics , quantum mechanics
With rapid accumulation of functional relationships between biological molecules, knowledge‐based networks have been constructed and stocked in many databases. These networks provide curated and comprehensive information for functional linkages among genes and proteins, whereas their activities are highly related with specific phenotypes and conditions. To evaluate a knowledge‐based network in a specific condition, the consistency between its structure and conditionally specific gene expression profiling data are an important criterion. In this study, the authors propose a Gaussian graphical model to evaluate the documented regulatory networks by the consistency between network architectures and time course gene expression profiles. They derive a dynamic Bayesian network model to evaluate gene regulatory networks in both simulated and true time course microarray data. The regulatory networks are evaluated by matching network structure with gene expression to achieve consistency measurement. To demonstrate the effectiveness of the authors method, they identify significant regulatory networks in response to the time course of circadian rhythm. The knowledge‐based networks are screened and ranked by their structural consistencies with dynamic gene expression profiling.