MOTIVATION: 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. AVAILABILITY AND IMPLEMENTATION: 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/.
MOTIVATION: 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. AVAILABILITY AND IMPLEMENTATION: 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: Kwang-Il Goh; Michael E Cusick; David Valle; Barton Childs; Marc Vidal; Albert-László Barabási Journal: Proc Natl Acad Sci U S A Date: 2007-05-14 Impact factor: 11.205
Authors: Kasper Lage; E Olof Karlberg; Zenia M Størling; Páll I Olason; Anders G Pedersen; Olga Rigina; Anders M Hinsby; Zeynep Tümer; Flemming Pociot; Niels Tommerup; Yves Moreau; Søren Brunak Journal: Nat Biotechnol Date: 2007-03 Impact factor: 54.908
Authors: Ian M Campbell; Mitchell Rao; Sean D Arredondo; Seema R Lalani; Zhilian Xia; Sung-Hae L Kang; Weimin Bi; Amy M Breman; Janice L Smith; Carlos A Bacino; Arthur L Beaudet; Ankita Patel; Sau Wai Cheung; James R Lupski; Paweł Stankiewicz; Melissa B Ramocki; Chad A Shaw Journal: PLoS Genet Date: 2013-09-26 Impact factor: 5.917