Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction using linked open data

Remzi Çelebi, Erkan Yasar, Huseyin Uyar, Ozgur Gumus, Oguz Dikenelli, Michel Dumontier

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Current approaches to identifying drug-drug interactions (DDIs), which involve clinical evaluation of drugs and post-marketing surveillance, are unable to provide complete, accurate information, nor do they alert the public to potentially dangerous DDIs before the drugs reach the market. Predicting potential drug-drug interaction helps reduce unanticipated drug interactions and drug development costs and optimizes the drug design process. Many bioinformatics databases have begun to present their data as Linked Open Data (LOD), a graph data model, using Semantic Web technologies. The knowledge graphs provide a powerful model for defining the data, in addition to making it possible to use underlying graph structure for extraction of meaningful information. In this work, we have applied Knowledge Graph (KG) Embedding approaches to extract feature vector representation of drugs using LOD to predict potential drug-drug interactions. We have investigated the effect of different embedding methods on the DDI prediction and showed that the knowledge embeddings are powerful predictors and comparable to current state-of-the-art methods for inferring new DDIs. We have applied Logistic Regression, Naive Bayes and Random Forest on Drugbank KG with the 10-fold traditional cross validation (CV) using RDF2Vec, TransE and TransD. RDF2Vec with uniform weighting surpass other embedding methods.
Original languageEnglish (US)
Title of host publication11th International Conference Semantic Web Applications and Tools for Life Sciences, SWAT4LS 2018
StatePublished - Jan 1 2018
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-30
Acknowledged KAUST grant number(s): FCC/1/1976-08-01, FCS/1/3657-02-01, URF/1/3454-01-01
Acknowledgements: The first named author (R.C.) is grateful to TUBITAK for providing financial support under 2214-A programme. This work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01, FCC/1/1976-08-01, and FCS/1/3657-02-01. This work was also supported by Ege University Research Fund through the 16-MUH-095 BAP project.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


Dive into the research topics of 'Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction using linked open data'. Together they form a unique fingerprint.

Cite this