Applying weighted network measures to microarray distance matrices

by: SE Ahnert, D Garlaschelli, TMA Fink, G Caldarelli

Journal of Physics A: Mathematical and Theoretical, Vol. 41, No. 22. (2008), 224011.

View FullText article

Reviews [Write a review of this article]

There are no reviews of this article

Find related articles from these CiteULike users

smc-infm (group), gcalda

Find related articles with these CiteULike tags

2008, caldarelli, no-tag, scale_free_networks, social_systems

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.

@article{citeulike:2879538, 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.}, author = {Ahnert, S. E. and Garlaschelli, D. and Fink, T. M. A. and Caldarelli, G. }, citeulike-article-id = {2879538}, doi = {10.1088/1751-8113/41/22/224011}, journal = {Journal of Physics A: Mathematical and Theoretical}, number = {22}, pages = {224011+}, posted-at = {2008-06-10 14:47:42}, priority = {2}, title = {Applying weighted network measures to microarray distance matrices}, url = {http://dx.doi.org/10.1088/1751-8113/41/22/224011}, volume = {41}, year = {2008} }

RIS record

TY - JOUR ID - citeulike:2879538 L3 - citeulike-article-id:2879538 TI - Applying weighted network measures to microarray distance matrices JF - Journal of Physics A: Mathematical and Theoretical VL - 41 IS - 22 SP - 224011 N2 - 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. AU - Ahnert, SE AU - Garlaschelli, D AU - Fink, TMA AU - Caldarelli, G PY - 2008/// UR - http://dx.doi.org/10.1088/1751-8113/41/22/224011 ER -

RIS BibTeX