Linked Life Data

17677101

Source:http://linkedlifedata.com/resource/pubmed/id/17677101

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5 Pt 1
pubmed:dateCreated
2007-8-6
pubmed:abstractText
The properties of certain networks are determined by hidden variables that are not explicitly measured. The conditional probability (propagator) that a vertex with a given value of the hidden variable is connected to k other vertices determines all measurable properties. We study hidden variable models and find an averaging approximation that enables us to obtain a general analytical result for the propagator. Analytic results showing the validity of the approximation are obtained. We apply hidden variable models to protein-protein interaction networks (PINs) in which the hidden variable is the association free energy, determined by distributions that depend on biochemistry and evolution. We compute degree distributions as well as clustering coefficients of several PINs of different species; good agreement with measured data is obtained. For the human interactome two different parameter sets give the same degree distributions, but the computed clustering coefficients differ by a factor of about 2. This shows that degree distributions are not sufficient to determine the properties of PINs.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1539-3755
pubmed:author
pubmed:issnType
Print
pubmed:volume
75
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
051910
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Clustering coefficients of protein-protein interaction networks.
pubmed:affiliation
Department of Physics, University of Washington Seattle, Seattle, WA 98195, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural