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BioMed Research International
Volume 2014, Article ID 686505, 16 pages
http://dx.doi.org/10.1155/2014/686505
Research Article

Network Analysis of Neurodegenerative Disease Highlights a Role of Toll-Like Receptor Signaling

1The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto, Italy
2Department of Mathematics, University of Trento, Via Sommarive, 14-38123 Povo, Italy

Received 5 September 2013; Revised 20 November 2013; Accepted 30 November 2013; Published 16 January 2014

Academic Editor: Stavros J. Hamodrakas

Copyright © 2014 Thanh-Phuong Nguyen 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

Suppl 1: The list of disease proteins and their corresponding diseases.

Suppl 2: The lists of disease proteins occurring in the six corresponding components.

Suppl 3: The computations and distribution diagrams for network centrality indices. Each worksheet presents one centrality.

Suppl 4: The list of connector proteins of the different diseases.

Suppl 5: The ranking of disease association using the network-based approach and the text mining approach. The same disease pair in the different rows is highlighted by the same color.

Suppl 6: Network of significantly enriched GO terms.

This schematic network illustrates GO terms that were significantly enriched in the ALS-PD (A) and FTD-PD (B) connector proteins, as well as the overlap between related terms. GO terms containing at least 10 connector proteins, and occurring in levels 3-8 of the GO hierarchy were considered in the analysis. Terms that were significantly enriched (p<0.05 after correction for multiple testing) with connector proteins are depicted. Node colours and inset bar indicate the p value for enrichment of each term, after correction for multiple testing.

  1. Supplementary Materials