Semantic Approaches to Enable Drug Discovery in Biomedical Big Data

Drugs such as penicillin and insulin have treated human disease for years, leading to both an improved quality of life, as well as an increase in overall life expectancy. Despite large amounts of biomedical data being available, the data is not being harnessed due to integration issues between datasets and heterogeneity between ontologies. Here, the use of semantic technologies in drug repurposing and drug safety is explored as it would greatly help the pharmaceutical industry answer difficult questions. Hypotheses studied include the use of mappings between model phenotypes and drug effects to identify human drug targets, and using pre-existing data as evidence to profile drug safety. Manual mappings were of better quality in comparison to automatic mappings. Evaluating drug related cardiotoxicity based on existing data was demonstrated to be successful for drug safety profiling. The results of this work demonstrate the usefulness of using pre-existing data to discover new knowledge.