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BioMed Research International
Volume 2013 (2013), Article ID 676328, 9 pages
http://dx.doi.org/10.1155/2013/676328
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.

Supplementary Material

Global citation summary.xlsx: This file integrates citation data for the three identified list of genes and correlates them with the related disease. These aggregated data enable to rank genes in a disease based on their citation count and to analyze the capability of our tool in selected genes that are highly cited in the literature with the related disease.

Disease_citation_heapmap.xlsx: This file reports raw citation data obtained using ProteinQuest to understand if, given a disease, the set of genes selected by our algorithm is highly cocited with the disease while showing low citation count with the other diseases. Data reported in this file are therefore obtained by running the query available in Algorithm 5 of the paper for the identified list of genes for the three considered diseases.

  1. Supplementary Materials