NeMo: Network Module identification in Cytoscape

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 to the CXC chemokine pathway and find a high scoring functional module of 12 disconnected phospholipase isoforms. Conclusion: We present a novel method that combines a unique neighbour-sharing score with hierarch...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Corban G. Rivera, Rachit Vakil, Joel S. Bader
• Keywords: Bipartite Network, Bipartite Network Modules, BMCBI 2010, MIPS Human Complexes