Applying weighted network measures to microarray distance matrices

TL;DR

This paper introduces a generalized method for analyzing weighted networks, applied to microarray data, which improves gene identification by using a new clustering coefficient measure.

Contribution

It presents a novel ensemble-based approach to extend unweighted network measures to weighted networks, specifically applied to microarray distance matrices.

Findings

The method effectively identifies biologically significant genes.

The clustering coefficient provides meaningful insights into network structure.

It outperforms existing gene identification approaches.

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 data sets from microarray experiments the clustering coefficient identifies a number of biologically significant genes, outperforming existing identification approaches.