NeMo: Network Module identification in Cytoscape.

ABSTRACT:

Background

As the size of the known human interactome grows, biologists increasingly rely on computational tools to identify patterns that represent protein complexes and pathways. Previous studies have shown that densely connected network components frequently correspond to community structure and functionally related modules. In this work, we present a novel method to identify densely connected and bipartite network modules based on a log odds score for shared neighbours.

Results

To evaluate the performance of our method (NeMo), we compare it to other widely used tools for community detection including kMetis, MCODE, and spectral clustering. We test these methods on a collection of synthetically constructed networks and the set of MIPS human complexes. We apply our method

SUBMITTER: Rivera CG 

PROVIDER: S-EPMC3009535 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature