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A unified representation of multiprotein complex data for modeling interaction networks
Author(s) -
Ding Chris,
He Xiaofeng,
Meraz Richard F.,
Holbrook Stephen R.
Publication year - 2004
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.20147
Subject(s) - multiprotein complex , computer science , bipartite graph , representation (politics) , cluster analysis , gene ontology , theoretical computer science , graph , interaction network , weighting , computational biology , data mining , artificial intelligence , biology , gene , genetics , gene expression , politics , law , medicine , radiology , political science
Abstract The protein interaction network presents one perspective for understanding cellular processes. Recent experiments employing high‐throughput mass spectrometric characterizations have resulted in large data sets of physiologically relevant multiprotein complexes. We present a unified representation of such data sets based on an underlying bipartite graph model that is an advance over existing models of the network. Our unified representation allows for weighting of connections between proteins shared in more than one complex, as well as addressing the higher level organization that occurs when the network is viewed as consisting of protein complexes that share components. This representation also allows for the application of the rigorous MinMaxCut graph clustering algorithm for the determination of relevant protein modules in the networks. Statistically significant annotations of clusters in the protein–protein and complex–complex networks using terms from the Gene Ontology indicate that this method will be useful for posing hypotheses about uncharacterized components of protein complexes or uncharacterized relationships between protein complexes. Proteins 2004. © 2004 Wiley‐Liss, Inc.