DiME: a scalable disease module identification algorithm with application to glioma progression.

ABSTRACT: Disease module is a group of molecular components that interact intensively in the disease specific biological network. Since the connectivity and activity of disease modules may shed light on the molecular mechanisms of pathogenesis and disease progression, their identification becomes one of the most important challenges in network medicine, an emerging paradigm to study complex human disease. This paper proposes a novel algorithm, DiME (Disease Module Extraction), to identify putative disease modules from biological networks. We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules. In addition, we have incorporated a st

SUBMITTER: Liu Y 

PROVIDER: S-EPMC3921127 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature