Journal
HEALTH INFORMATICS JOURNAL
Volume 26, Issue 4, Pages 2737-2750Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/1460458220937101
Keywords
drug repurposing; graph embedding; knowledge graph; machine learning; meta path
Funding
- Ministry of Education of the Republic of Korea
- National Research Foundation of Korea [NRF-2019S1A5A8033338]
- National Research Foundation of Korea [2019S1A5A8033338] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Due to the huge costs associated with new drug discovery and development, drug repurposing has become an important complement to the traditional de novo approach. With the increasing number of public databases and the rapid development of analytical methodologies, computational approaches have gained great momentum in the field of drug repurposing. In this study, we introduce an approach to knowledge-driven drug repurposing based on a comprehensive drug knowledge graph. We design and develop a drug knowledge graph by systematically integrating multiple drug knowledge bases. We describe path- and embedding-based data representation methods of transforming information in the drug knowledge graph into valuable inputs to allow machine learning models to predict drug repurposing candidates. The evaluation demonstrates that the knowledge-driven approach can produce high predictive results for known diabetes mellitus treatments by only using treatment information on other diseases. In addition, this approach supports exploratory investigation through the review of meta paths that connect drugs with diseases. This knowledge-driven approach is an effective drug repurposing strategy supporting large-scale prediction and the investigation of case studies.
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