|Identifying communities from multiplex biological networks. |
(Article) Publié: Peerj, vol. 3:e1525 p. (2015)
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PubMed Central: PMC4690346
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Various biological networks can be constructed, each featuring gene/proteinrelationships of different meanings (e.g., protein interactions or geneco-expression). However, this diversity is classically not considered and thedifferent interaction categories are usually aggregated in a single network. The multiplex framework, where biological relationships are represented by different network layers reflecting the various nature of interactions, is expected toretain more information. Here we assessed aggregation, consensus andmultiplex-modularity approaches to detect communities from multiple networksources. By simulating random networks, we demonstrated that themultiplex-modularity method outperforms the aggregation and consensus approaches when network layers are incomplete or heterogeneous in density. Application to a multiplex biological network containing 4 layers of physical or functionalinteractions allowed recovering communities more accurately annotated than their aggregated counterparts. Overall, taking into account the multiplexity ofbiological networks leads to better-defined functional modules. A user-friendlygraphical software to detect communities from multiplex networks, andcorresponding C source codes, are available at GitHub(https://github.com/gilles-didier/MolTi).