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This article is part of the supplement: Selected articles from the Second Annual Translational Bioinformatics Conference (TBC 2012)

Open Access Research

Compensating for literature annotation bias when predicting novel drug-disease relationships through Medical Subject Heading Over-representation Profile (MeSHOP) similarity

Warren A Cheung12, BF Francis Ouellette34* and Wyeth W Wasserman15*

Author Affiliations

1 Centre for Molecular Medicine and Therapeutics at the Child and Family Research Institute, University of British Columbia, Vancouver, BC, Canada

2 Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada

3 Ontario Institute for Cancer Research, Toronto, ON, Canada

4 Department of Cells and Systems Biology, University of Toronto, Toronto, ON, Canada

5 Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada

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BMC Medical Genomics 2013, 6(Suppl 2):S3  doi:10.1186/1755-8794-6-S2-S3

Published: 7 May 2013

Additional files

Additional file 1:

Comparison of drug-disease candidates for five disorders. The top 20 drug candidates for gout, cardiac arrhythmia, lupus, jaundice and asthma are provided. We contrast the corrected and uncorrected drug candidate lists for each disorder. The uncorrected list is heavily biased to general compounds such as Monoclonal Antibodies, Norepinephrine and Iron, whereas the corrected drug candidates focus on drugs that are much more specific to the disorder. This file is in Excel format.

Format: XLS Size: 75KB Download file

This file can be viewed with: Microsoft Excel Viewer

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