Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications

Mona Alshahrani*, Abdullah Almansour, Asma Alkhaldi, Maha A. Thafar, Mahmut Uludag, Magbubah Essack, Robert Hoehndorf

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug targets and indications. To effectively utilize information in published literature, we integrate knowledge graphs and published literature using named entity recognition and normalization before applying a machine learning model that utilizes the combination of graph and literature. We then use supervised machine learning to show the effects of combining features from biomedical knowledge and published literature on the prediction of drug targets and drug indications. We demonstrate that our approach using datasets for drug-target interactions and drug indications is scalable to large graphs and can be used to improve the ranking of targets and indications by exploiting features from either structure or unstructured information alone.

Original languageEnglish (US)
Article numbere13061
StatePublished - Apr 2022

Bibliographical note

Publisher Copyright:
Copyright 2022 Alshahrani et al.


  • Bio-ontologies
  • Biomedical knowledge graphs
  • Biomedical literature
  • Drug-indications
  • Drug-target interactions
  • Linked Data
  • Multi-modal learning

ASJC Scopus subject areas

  • General Neuroscience
  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences


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