Email updates

Keep up to date with the latest news and content from BioData Mining and BioMed Central.

Open Access Research

Mining tissue specificity, gene connectivity and disease association to reveal a set of genes that modify the action of disease causing genes

Antonio Reverter*, Aaron Ingham and Brian P Dalrymple

Author Affiliations

Computational and Systems Biology, CSIRO Livestock Industries, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, Brisbane, Queensland 4067, Australia

For all author emails, please log on.

BioData Mining 2008, 1:8  doi:10.1186/1756-0381-1-8

Published: 19 September 2008

Additional files

Additional file 1:

Additional Table 1: The set of 15,050 genes. List of 15,050 genes included in the analyses. For each gene, the number of tissues (out of 32) in which the gene is being expressed, its average expression, disease association and connectivity structure is provided.

Format: XLS Size: 1.9MB Download file

This file can be viewed with: Microsoft Excel Viewer

Open Data

Additional file 2:

Additional Table 2: Set of 112 guilt-by-association genes. List of 112 genes not associated with disease according to OMIM yet with high connectivity with disease-associated genes. For each gene, the proportion of disease genes among connectors and polymorphism or differential expression associated with disease along with the relevant literature reference is provided.

Format: DOC Size: 208KB Download file

This file can be viewed with: Microsoft Word Viewer

Open Data