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Title: Mining heterogeneous network for drug repositioning using phenotypic information extracted from social media and pharmaceutical databases
Drug repositioning has drawn significant attention for drug development in pharmaceutical research and industry, because of its advantages in cost and time compared with the de novo drug development. The availability of biomedical databases and online health-related information, as well as the high-performance computing, empowers the development of computational drug repositioning methods. In this work, we developed a systematic approach that identifies repositioning drugs based on heterogeneous network mining using both pharmaceutical databases (PharmGKB and SIDER) and online health community (MedHelp). By utilizing adverse drug reactions (ADRs) as the intermediate, we constructed a heterogeneous health network containing drugs, diseases, and ADRs, and developed path-based heterogeneous network mining approaches for drug repositioning. Additionally, we investigated on how the data sources affect the performance on drug repositioning. Experiment results showed that combining both PharmKGB and MedHelp identified 479 repositioning drugs, which are more than the repositioning drugs discovered by other alternatives. In addition, 31% of the 479 of the discovered repositioning drugs were supported by evidence from PubMed.  more » « less
Award ID(s):
1741306
NSF-PAR ID:
10125495
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Artificial intelligence in medicine
Volume:
96
ISSN:
0933-3657
Page Range / eLocation ID:
80 - 92
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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