Deterministic graph-theoretic algorithm for detecting modules in biological interaction networks

Authors:
Roger L. Chang;Feng Luo;Stuart Johnson;Richard H. Scheuermann
Affiliations:
Bioinformatics and Systems Biology Graduate Program, University of California San Diego, 9500 Gilman Dr., La Jolla, San Diego, CA 92093-0412, USA.;School of Computing, Clemson University, 310 McAdams Hall, Clemson, SC 29634-0974, USA.;Texas Advanced Computing Center, University of Texas Austin, 10100 Burnet Road (R8700), Austin, TX 78758-4497, USA.;Department of Pathology, Division of Biomedical Informatics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, Texas 75390-9072, USA
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2010

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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.