Applying weighted network measures to microarray distance matrices (Articolo in rivista)

Type
Label
  • Applying weighted network measures to microarray distance matrices (Articolo in rivista) (literal)
Anno
  • 2008-01-01T00:00:00+01:00 (literal)
Alternative label
  • Ahnert S.E., Garlaschelli D., Fink T.M.A., Caldarelli G. (2008)
    Applying weighted network measures to microarray distance matrices
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Ahnert S.E., Garlaschelli D., Fink T.M.A., Caldarelli G. (literal)
Pagina inizio
  • 224011 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 41 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • fasc. (22). IOP Publishing. (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • \"[Ahnert, S. E.] Cavendish Lab, Condensed Matter Theory Grp, Cambridge CB3 0HE, England; [Garlaschelli, D.] Univ Siena, Dipartimento Fis, I-53100 Siena, Italy; [Fink, T. M. A.] Inst Curie, CNRS, UMR 144, F-75248 Paris, France; [Caldarelli, G.] Univ Roma La Sapienza, INFM, CNR, Ist Sistemi Complessi, I-00185 Rome, Italy; [Caldarelli, G.] Univ Roma La Sapienza, INFM, CNR, Dipartimento Fis, I-00185 Rome, Italy; [Caldarelli, G.] Ctr Studi, I-00185 Rome, Italy; [Caldarelli, G.] Museo Fis Enr Fermi, I-00185 Rome, Italy (literal)
Titolo
  • Applying weighted network measures to microarray distance matrices (literal)
Abstract
  • In recent work we presented a new approach to the analysis of weighted networks, by providing a straightforward generalization of any network measure defined on unweighted networks. This approach is based on the translation of a weighted network into an ensemble of edges, and is particularly suited to the analysis of fully connected weighted networks. Here we apply our method to several such networks including distance matrices, and show that the clustering coefficient, constructed by using the ensemble approach, provides meaningful insights into the systems studied. In the particular case of two datasets from microarray experiments the clustering coefficient identifies a number of biologically significant genes, outperforming existing identification approaches. (literal)
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