DIDO: a disease-determinants ontology from web sources
Proceedings of the 20th international conference companion on World wide web
Semantics-aware open information extraction in the biomedical domain
Proceedings of the 4th International Workshop on Semantic Web Applications and Tools for the Life Sciences
Evaluating scientific hypotheses using the SPARQL inferencing notation
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
Hi-index | 3.84 |
Motivation: Identifying drug–drug interactions (DDIs) is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of DDIs, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this article, we propose a novel approach that integrates text mining and automated reasoning to derive DDIs. Through the extraction of various facts of drug metabolism, not only the DDIs that are explicitly mentioned in text can be extracted but also the potential interactions that can be inferred by reasoning. Results: Our approach was able to find several potential DDIs that are not present in DrugBank. We manually evaluated these interactions based on their supporting evidences, and our analysis revealed that 81.3% of these interactions are determined to be correct. This suggests that our approach can uncover potential DDIs with scientific evidences explaining the mechanism of the interactions. Contact: luis.tari@roche.com