Journal
PEERJ
Volume 10, Issue -, Pages -Publisher
PEERJ INC
DOI: 10.7717/peerj.13061
Keywords
Biomedical literature; Biomedical knowledge graphs; Drug-target interactions; Drug-indications; Multi-modal learning; Bio-ontologies; Linked Data
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Funding
- National Center of Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Saudi Arabia
- King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [FCC/1/1976-20-01]
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This article introduces a machine learning method that predicts drug targets and indications by combining information from knowledge graphs and published literature. By integrating different types of information, the ranking of targets and indications can be improved.
Biomedical knowledge is represented in structured databases and published in biomed-ical 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.
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