Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-LSTM network

Md Rezaul Karim, Michael Cochez, Joao Bosco Jares, Mamtaz Uddin, Oya Beyan, Stefan Decker

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Interference between pharmacological substances can cause serious medical injuries. Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases but can also result in a reduction of drug development cost. Presently, most drug-related knowledge is the result of clinical evaluations and post marketing surveillance; resulting in a limited amount of information. Existing data-driven prediction approaches for DDIs typically rely on a single source of information, while using information from multiple sources would help improve predictions. Machine learning (ML) techniques are used, but the techniques are often unable to deal with skew in the data. Hence, we propose a new ML approach for predicting DDIs based on multiple data sources. For this task we use 12,000 drug features from DrugBank, PharmGKB, and KEGG drugs, which are integrated using Knowledge Graphs (KGs). To train our prediction model, we first embed the nodes in the graph using various embedding approaches. We found that the best performing combination was a ComplEx embedding method creating using PyTorch-BigGraph (PBG) with a Convolutional-LSTM network and classic machine learning based prediction models. The model averaging ensemble method of three best classifiers yields up to 0.94, 0.92, 0.80 for AUPR, F1-score, and MCC, respectively during 5-fold cross-validation tests.

Original languageEnglish
Title of host publicationACM-BCB 2019 - Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages113-123
Number of pages11
ISBN (Electronic)9781450366663
DOIs
Publication statusPublished - 4 Sep 2019
Externally publishedYes
Event10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2019 - Niagara Falls, United States
Duration: 7 Sep 201910 Sep 2019

Publication series

NameACM-BCB 2019 - Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Conference

Conference10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2019
Country/TerritoryUnited States
CityNiagara Falls
Period7/09/1910/09/19

Keywords

  • Conv-LSTM network
  • Drug-drug interactions
  • Graph embeddings
  • Knowledge graphs
  • Linked data
  • Model averaging ensemble

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