TimeXNet: identifying active gene sub-networks using time-course gene expression profiles.

ABSTRACT:

Background

Time-course gene expression profiles are frequently used to provide insight into the changes in cellular state over time and to infer the molecular pathways involved. When combined with large-scale molecular interaction networks, such data can provide information about the dynamics of cellular response to stimulus. However, few tools are currently available to predict a single active gene sub-network from time-course gene expression profiles.

Results

We introduce a tool, TimeXNet, which identifies active gene sub-networks with temporal paths using time-course gene expression profiles in the context of a weighted gene regulatory and protein-protein interaction network. TimeXNet uses a specialized form of the network flow optimization approach to identify the most p

SUBMITTER: Patil A 

PROVIDER: S-EPMC4290689 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature