Abstract: Medical Subject Heading Overrepresentation Profiles (MeSHOPs) quantitatively summarise the literature associated with biological entities such as diseases or drugs. A profile is constructed by counting the number of times each MeSH term is assigned to an entity-related research publication in the MEDLINE/PUBMED database and calculating the significance of the count relative to a background expectation. 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. 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. By explicitly accounting for the annotation bias, unexpectedly similar drug-disease pairs are highlighted as candidates for drug repositioning research.

This website provides supplementary materials for the paper appearing in BMC Medical Genomics.

Browse Drug-Disease Predictions

The predicted drug-disease similarity can be browsed interactively here.

Supplementary Materials

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Comparison of Drug-Disease Candidates for Five Disorders

Download Supplementary Table 1 as an Excel Spreadsheet: 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. Size: 76288 bytes, md5sum: c7b61a862a102216f56cf83cc4a910a2.

Table of Scores for All Drug-Disease Relationships

Download Supplementary Table 2 as a gzip compressed delimited text file: This file provides a table of each drug, disease relationship with raw similarity score and corrected similarity score. WARNING - the file is 617MB, compressed using gzip. Fields are delimited using the vertical pipe character ( | ). Each line specifies the disease name, the drug name, the uncorrected score and the corrected score. Browse the relationships interactively here. Size: 646737160 bytes, md5sum: 52110b2b9e602ce19df8edd25545e808.

Browse MeSHOPs

Examine all terms associated with a disease, or all diseases associated with a term.
Examine all terms associated with a chemical compound, or all chemical compounds associated with a term.

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