Source:http://linkedlifedata.com/resource/pubmed/id/20223734
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Predicate | Object |
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rdf:type | |
lifeskim:mentions | |
pubmed:issue |
2
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pubmed:dateCreated |
2010-3-12
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pubmed:abstractText |
An approach for module identification, Modules of Networks (MoNet), introduced an intuitive module definition and clear detection method using edges ranked by the Girvan-Newman algorithm. Modules from a yeast network showed significant association with biological processes, indicating the method's utility; however, systematic bias leads to varied results across trials. MoNet modules also exclude some network regions. To address these shortcomings, we developed a deterministic version of the Girvan-Newman algorithm and a new agglomerative algorithm, Deterministic Modularization of Networks (dMoNet). dMoNet simultaneously processes structurally equivalent edges while preserving intuitive foundations of the MoNet algorithm and generates modules with full network coverage.
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pubmed:grant | |
pubmed:language |
eng
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pubmed:journal | |
pubmed:citationSubset |
IM
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pubmed:chemical | |
pubmed:status |
MEDLINE
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pubmed:issn |
1744-5485
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pubmed:author | |
pubmed:issnType |
Print
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pubmed:volume |
6
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
101-19
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pubmed:meshHeading | |
pubmed:year |
2010
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pubmed:articleTitle |
Deterministic graph-theoretic algorithm for detecting modules in biological interaction networks.
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pubmed:affiliation |
University of California San Diego, La Jolla, San Diego, CA 92093-0412, USA. rlchang@ucsd.edu
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pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, Non-P.H.S.,
Research Support, N.I.H., Extramural
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