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Drug-Drug Interaction Prediction on a Biomedical Literature Knowledge Graph

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12299)


Knowledge Graphs provide insights from data extracted in various domains. In this paper, we present an approach discovering probable drug-to-drug interactions, through the generation of a Knowledge Graph from disease-specific literature. The Graph is generated using natural language processing and semantic indexing of biomedical publications and open resources. The semantic paths connecting different drugs in the Graph are extracted and aggregated into feature vectors representing drug pairs. A classifier is trained on known interactions, extracted from a manually curated drug database used as a golden standard, and discovers new possible interacting pairs. We evaluate this approach on two use cases, Alzheimer’s Disease and Lung Cancer. Our system is shown to outperform competing graph embedding approaches, while also identifying new drug-drug interactions that are validated retrospectively.


  • Literature mining
  • Knowledge graph
  • Path analysis
  • Knowledge discovery
  • Drug-drug interactions

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This work is supported by European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727658, project iASiS ( (Integration and analysis of heterogeneous big data for precision medicine and suggested treatments for different types of patients). We would also like to acknowledge the help we received from the iASiS consortium for the completion of this work.

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Correspondence to Konstantinos Bougiatiotis .

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Bougiatiotis, K., Aisopos, F., Nentidis, A., Krithara, A., Paliouras, G. (2020). Drug-Drug Interaction Prediction on a Biomedical Literature Knowledge Graph. In: Michalowski, M., Moskovitch, R. (eds) Artificial Intelligence in Medicine. AIME 2020. Lecture Notes in Computer Science(), vol 12299. Springer, Cham.

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  • Print ISBN: 978-3-030-59136-6

  • Online ISBN: 978-3-030-59137-3

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