Gaussian graphical model for identifying significantly responsive regulatory networks from time course high-throughput data.

ABSTRACT: 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

SUBMITTER: Liu ZP 

PROVIDER: S-EPMC8687204 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature