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NetComm: a network analysis tool based on communicability
Author(s) -
Ian M. Campbell,
Regis A. James,
Edward S. Chen,
Chad A. Shaw
Publication year - 2014
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btu536
Subject(s) - computer science , biological network , metric (unit) , clique , network analysis , set (abstract data type) , similarity (geometry) , theoretical computer science , data mining , machine learning , computational biology , artificial intelligence , biology , mathematics , operations management , physics , combinatorics , quantum mechanics , economics , image (mathematics) , programming language
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.

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