%0 Journal Article
%@ 1751-8113
%A Ahnert, Sebastian E.
%A Garlaschelli, Diego
%A Fink, Thomas M.A.
%A Caldarelli, Guido
%D 2008
%F eprints:1094
%I Institute of Physics
%J Journal of Physics A: Mathematical and Theoretical
%K PACS: 87.16.Yc Regulatory genetic and chemical networks; 02.10.Yn Matrix theory; 87.10.-e General theory and mathematical aspects; 87.80.-y Biophysical techniques (research methods)  
%N 22
%P 224011
%T Applying weighted network measures to microarray distance matrices
%U http://eprints.imtlucca.it/1094/
%V 41
%X 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.