Predicting Multi-Type Drug-Drug Interactions Using a Disease-Specific Knowledge Graph

Drug-drug interactions are a major cause of mortality during hospitalization, causing toxicities and unexpected side effects. This work utilizes a knowledge graph derived from biomedical literature and open databases, to predict different classes of drug-drug interactions. To this end, a path analysisbased machine learning approach is compared with various graph embedding techniques in a lung cancer use case. The experiments aim at analysing different type of interactions from a public database, to define five general classes. Focusing on under-represented classes of interactions, in order to boost their predictive performance via different configurations, the path analysis-based approach achieves better performance results, allowing for their subsequent interpretation using the most important features of each path.