title: NetComm: a network analysis tool based on communicability.
abstract: BACKGROUND
  Set-based network similarity metrics are increasingly used to productively analyze genome-wide data. Conventional approaches, such as mean shortest path and clique-based metrics, have been useful but are not well suited to all applications. Computational scientists in other disciplines have developed communicability as a complementary metric. Network communicability considers all paths of all lengths between two network members. Given the success of previous network analyses of protein-protein interactions, we applied the concepts of network communicability to this problem. Here we show that our communicability implementation has advantages over traditional approaches. Overall, analyses suggest network communicability has considerable utility in analysis of large-scale biological networks.
  
  
  METHODS
  We provide our method as an R package for use in both human protein-protein interaction network analyses and analyses of arbitrary networks along with a tutorial at http://www.shawlab.org/NetComm/.
authors.affiliation: Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77054, USA.
authors.forename: Ian M
authors.initials: IM
authors.lastname: Campbell
authors.affiliation: Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77054, USA.
authors.forename: Regis A
authors.initials: RA
authors.lastname: James
authors.affiliation: Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77054, USA.
authors.forename: Edward S
authors.initials: ES
authors.lastname: Chen
authors.affiliation: Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77054, USA.
authors.forename: Chad A
authors.initials: CA
authors.lastname: Shaw
mesh_terms: D004194:Disease; D006801:Humans; D025941:Protein Interaction Mapping / Q000379:methods*; D012984:Software