biostudies-literatureCampbell IMNINDS NIH HHSNIGMS NIH HHS3387-9https://www.ebi.ac.uk/biostudies/studies/S-EPMC4274347biostudies-literatureUnknown30(23)<h4>Motivation</h4>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.<h4>Availability and implementation</h4>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/.Bioinformatics (Oxford, England)NetComm: a network analysis tool based on communicability.PMC4274347R25 GM056929K12 GM084897T32 GM007330F31 NS083159Campbell IMShaw CAChen ESJames RAfalseNetComm: a network analysis tool based on communicability.<h4>Motivation</h4>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.<h4>Availability and implementation</h4>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/.2014-01-01T00:00:00Z2014 Dec2021-02-21T01:26:30Z2019-03-27T01:42:14ZS-EPMC42743472512389910.1093/bioinformatics/btu536