Revealing biological modules via graph summarization

Navlakha, S., Schatz, M. C., Kingsford, C. (February 2009) Revealing biological modules via graph summarization. J Comput Biol, 16 (2). pp. 253-64. ISSN 1066-5277

DOI: 10.1089/cmb.2008.11TT


The division of a protein interaction network into biologically meaningful modules can aid with automated detection of protein complexes and prediction of biological processes and can uncover the global organization of the cell. We propose the use of a graph summarization (GS) technique, based on graph compression, to cluster protein interaction graphs into biologically relevant modules. The method is motivated by defining a biological module as a set of proteins that have similar sets of interaction partners. We show this definition, put into practice by a GS algorithm, reveals modules that are more biologically enriched than those found by other methods. We also apply GS to predict complex memberships, biological processes, and co-complexed pairs and show that in most settings GS is preferable over existing methods of protein interaction graph clustering.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > design > protein network design
bioinformatics > computational biology > algorithms
bioinformatics > computational biology
CSHL Authors:
Communities: CSHL labs > Navlakha lab
Depositing User: Matthew Dunn
Date: 4 February 2009
Date Deposited: 08 Nov 2019 15:04
Last Modified: 08 Nov 2019 15:04
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