Literature-based predictions of treatments for genetic disease pathology

Abstract

AbstractIdentifying genetic modifiers of disease-causing genes can guide drug discovery for treatment of genetic disorders, but selecting promising drugs to test requires extensive and up-to-date knowledge of drug-gene and gene-gene relationships. To address this challenge, we present PARsing ModifiErS via Abstract aNnotations (PARMESAN), a computational tool that searches PubMed for information on these relationships, and assembles them into one knowledgebase. PARMESAN then hypothesizes on undiscovered drug-gene relationships, assigning an evidence-based score to each hypothesis. We compare PARMESAN’s drug-gene hypotheses to all of the drug-gene relationships displayed by DrugBank, and see a strong correlation between the prediction score and the predictive accuracy—such that predictions scoring above 10 are 11 times more likely to be correct than incorrect. This publicly available tool provides an automated way to prioritize drug screens to target the most-promising drugs to test, thereby saving time and resources in the development of therapeutics for genetic disorders.