Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2013 (2013), Article ID 676328, 9 pages
Research Article

Reducing the Complexity of Complex Gene Coexpression Networks by Coupling Multiweighted Labeling with Topological Analysis

1Department of Controls and Computer Engineering, Politecnico di Torino, 10129 Torino, Italy
2Consorzio Interuniversitario Nazionale per l’Informatica, 11029 Verres, Italy
3Department of Agriculture, Forest and Food Sciences, Università degli Studi di Torino, 10124 Torino, Italy

Received 30 April 2013; Accepted 25 July 2013

Academic Editor: Sarah H. Elsea

Copyright © 2013 Alfredo Benso et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Undirected gene coexpression networks obtained from experimental expression data coupled with efficient computational procedures are increasingly used to identify potentially relevant biological information (e.g., biomarkers) for a particular disease. However, coexpression networks built from experimental expression data are in general large highly connected networks with an elevated number of false-positive interactions (nodes and edges). In order to infer relevant information, the network must be properly filtered and its complexity reduced. Given the complexity and the multivariate nature of the information contained in the network, this requires the development and application of efficient feature selection algorithms to be able to exploit the topological characteristics of the network to identify relevant nodes and edges. This paper proposes an efficient multivariate filtering designed to analyze the topological properties of a coexpression network in order to identify potential relevant genes for a given disease. The algorithm has been tested on three datasets for three well known and studied diseases: acute myeloid leukemia, breast cancer, and diffuse large B-cell lymphoma. Results have been validated resorting to bibliographic data automatically mined using the ProteinQuest literature mining tool.