Sciweavers

Share
SAC
2015
ACM

Discovering weighted motifs in gene co-expression networks

3 years 5 months ago
Discovering weighted motifs in gene co-expression networks
An important dimension of complex networks is embedded in the weights of its edges. Incorporating this source of information on the analysis of a network can greatly enhance our understanding of it. This is the case for gene co-expression networks, which encapsulate information about the strength of correlation between gene expression profiles. Classical unweighted gene co-expression networks use thresholding for defining connectivity, losing some of the information contained in the different connection strengths. In this paper, we propose a mining method capable of extracting information from weighted gene co-expression networks. We study groups of differently connected nodes and their importance as network motifs. We define a subgraph as a motif if the weights of edges inside the subgraph hold a significantly different distribution than what would be found in a random distribution. We use the Kolmogorov-Smirnov test to calculate the significance score of the subgraph, avoidin...
Sarvenaz Choobdar, Pedro Manuel Pinto Ribeiro, Fer
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where SAC
Authors Sarvenaz Choobdar, Pedro Manuel Pinto Ribeiro, Fernando M. A. Silva
Comments (0)
books