SEQUOIA: significance enhanced network querying through context-sensitive random walk and minimization of network conductance.

ABSTRACT:

Background

Network querying algorithms provide computational means to identify conserved network modules in large-scale biological networks that are similar to known functional modules, such as pathways or molecular complexes. Two main challenges for network querying algorithms are the high computational complexity of detecting potential isomorphism between the query and the target graphs and ensuring the biological significance of the query results.

Results

In this paper, we propose SEQUOIA, a novel network querying algorithm that effectively addresses these issues by utilizing a context-sensitive random walk (CSRW) model for network comparison and minimizing the network conductance of potential matches in the target network. The CSRW model, inspired by the pair hidden Mark

SUBMITTER: Jeong H 

PROVIDER: S-EPMC5374659 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature