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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

Abstract

Background

Using annotations to the articles in MEDLINE®/PubMed®, over six thousand chemical compounds with pharmacological actions have been tracked since 1996. Medical Subject Heading Over-representation Profiles (MeSHOPs) quantitatively leverage the literature associated with biological entities such as diseases or drugs, providing the opportunity to reposition known compounds towards novel disease applications.

Methods

A MeSHOP is constructed by counting the number of times each medical subject term is assigned to an entity-related research publication in the MEDLINE database and calculating the significance of the count by comparing against the count of the term in a background set of publications. Based on the expectation that drugs suitable for treatment of a disease (or disease symptom) will have similar annotation properties to the disease, we successfully predict drug-disease associations by comparing MeSHOPs of diseases and drugs.

Results

The MeSHOP comparison approach delivers an 11% improvement over bibliometric baselines. However, novel drug-disease associations are observed to be biased towards drugs and diseases with more publications. To account for the annotation biases, a correction procedure is introduced and evaluated.

Conclusions

By explicitly accounting for the annotation bias, unexpectedly similar drug-disease pairs are highlighted as candidates for drug repositioning research. MeSHOPs are shown to provide a literature-supported perspective for discovery of new links between drugs and diseases based on pre-existing knowledge.