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  • Biological network comparison using graphlet degree distribution
    Pržulj, Nataša, 1973-
    Motivation: Analogous to biological sequence comparison, comparing cellular networks is an important problem that could provide insight into biological understanding and therapeutics. For technical ... reasons, comparing large networks is computationally infeasible, and thus heuristics, such as the degree distribution, clustering coefficient, diameter, and relative graphlet frequency distribution have been sought. It is easy to demonstrate that two networks are different by simply showing a short list of properties in which they differ. It is much harder to show that two networks are similar, as it requires demonstrating their similarity in all of their exponentially many properties. Clearly, it is computationally prohibitive to analyze all network properties, but the larger the number of constraints we impose in determining network similarity, the more likely it is that the networks will truly be similar. Results: We introduce a new systematic measure of a network's local structure that imposes a large number of similarity constraints on networks being compared. In particular, we generalize the degree distribution, which measures the number of nodes Žtouchingʼ k edges, into distributions measuring the number of nodes Žtouchingʼ k graphlets, where graphlets are small connected non-isomorphic subgraphs of a large network. Our new measure of network local structure consists of 73 graphlet degree distributions of graphlets with 2-5 nodes, but it is easily extendible to a greater number of constraints (i.e. graphlets), if necessary, and the extensions are limited only by the available CPU. Furthermore, we show a way to combine the 73 graphlet degree distributions into a network Žagreementʼ measure which is a number between 0 and 1, where 1 means that networks have identical distributions and 0 means that they are far apart. Based on this new network agreement measure, we show that almost all of the 14 eukaryotic PPI networks, including human, resulting from various high-throughput experimental techniques, as well as from curated databases, are better modeled by geometricrandom graphs than by Erdös-Rény, random scale-free, or Barabási-Albert scale-free networks.
    Source: Bioinformatics. - ISSN 1367-4803 (Vol. 23, no. 2, 2007, str. e177-e183)
    Type of material - article, component part ; adult, serious
    Publish date - 2007
    Language - english
    COBISS.SI-ID - 1024349249

    Link(s):

    http://bioinformatics.oxfordjournals.org/content/23/2/e177.full.pdf+html
    DOI

source: Bioinformatics. - ISSN 1367-4803 (Vol. 23, no. 2, 2007, str. e177-e183)
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