2016
DOI: 10.1038/srep32745
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Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing

Abstract: Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we li… Show more

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Cited by 47 publications
(35 citation statements)
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“…When using network clustering, if a drug does not comply with the community/cluster label, then this indicates a possible repurposing [ 48 ]. We labeled the clusters using the drug properties listed by DrugBank or reported in the literature, such that the dominant property or properties (i.e., properties found in more than 50% of the drugs in the community) give the name of the community, as indicated in Table 1 and Table 2 .…”
Section: Resultsmentioning
confidence: 99%
“…When using network clustering, if a drug does not comply with the community/cluster label, then this indicates a possible repurposing [ 48 ]. We labeled the clusters using the drug properties listed by DrugBank or reported in the literature, such that the dominant property or properties (i.e., properties found in more than 50% of the drugs in the community) give the name of the community, as indicated in Table 1 and Table 2 .…”
Section: Resultsmentioning
confidence: 99%
“…For instance, future studies should implement directed signaling networks (e.g., KEGG signaling network). Also, while affinity propagation is a wellestablished unsupervised clustering method that has been successfully applied to constructing communities based on drug-drug similarity metrics [17], other methods may be explored including Markov clustering and other energy-model layout algorithms [18]. Second, we can include more extensive drug datasets, as expanding known drug-target interactions will also likely increase the method performance.…”
Section: Discussionmentioning
confidence: 99%
“…Various studies have already been performed in pharmacology with interesting applications of complex networks, including DDIs prediction (e.g., [ 17 , 18 ]). There are three main benefits of processing DDIs with network analysis approach [ 19 ]: (i) researcher can predict potential, previously unknown, DDIs; (ii) certain (insignificant) DDIs will be avoided in such knowledge representation; and (iii) relationships which link pharmacodynamic and pharmacokinetic drug characteristics to DDIs can be explored.…”
Section: Introductionmentioning
confidence: 99%