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.
%0 Journal Article
%1 Chang2010Deterministic
%A Chang, Roger L.
%A Luo, Feng
%A Johnson, Stuart
%A Scheuermann, Richard H.
%D 2010
%J International journal of bioinformatics research and applications
%K algorithms graph-theory modularity networks
%N 2
%P 101--119
%T Deterministic graph-theoretic algorithm for detecting modules in biological interaction networks.
%U http://view.ncbi.nlm.nih.gov/pubmed/20223734
%V 6
%X 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.
@article{Chang2010Deterministic,
abstract = {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.},
added-at = {2018-12-02T16:09:07.000+0100},
author = {Chang, Roger L. and Luo, Feng and Johnson, Stuart and Scheuermann, Richard H.},
biburl = {https://www.bibsonomy.org/bibtex/233191f826da796ce5bd152c2002f3310/karthikraman},
citeulike-article-id = {6845394},
citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/20223734},
citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=20223734},
interhash = {5a6fa56a198e68d224950be9bdfd775e},
intrahash = {33191f826da796ce5bd152c2002f3310},
issn = {1744-5485},
journal = {International journal of bioinformatics research and applications},
keywords = {algorithms graph-theory modularity networks},
number = 2,
pages = {101--119},
pmid = {20223734},
posted-at = {2010-03-14 14:33:49},
priority = {2},
timestamp = {2018-12-02T16:09:07.000+0100},
title = {Deterministic graph-theoretic algorithm for detecting modules in biological interaction networks.},
url = {http://view.ncbi.nlm.nih.gov/pubmed/20223734},
volume = 6,
year = 2010
}