Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings.

ABSTRACT:

Background

Current approaches to identifying drug-drug interactions (DDIs), include safety studies during drug development and post-marketing surveillance after approval, offer important opportunities to identify potential safety issues, but are unable to provide complete set of all possible DDIs. Thus, the drug discovery researchers and healthcare professionals might not be fully aware of potentially dangerous DDIs. Predicting potential drug-drug interaction helps reduce unanticipated drug interactions and drug development costs and optimizes the drug design process. Methods for prediction of DDIs have the tendency to report high accuracy but still have little impact on translational research due to systematic biases induced by networked/paired data. In this work, we aimed to pres

SUBMITTER: Celebi R 

PROVIDER: S-EPMC6921491 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature