Finding melanoma drugs through a probabilistic knowledge graph

Authors
McCusker, Jamie
Dumontier, Michel
Yan, Rui
He, Sylvia
McGuinness, Deborah L.
ORCID
No Thumbnail Available
Other Contributors
Issue Date
2017-02-17
Keywords
Repurposing Drugs with Semantics
Degree
Terms of Use
Full Citation
Abstract
Metastatic cutaneous melanoma is an aggressive skin cancer with some progressionslowing treatments but no known cure. The omics data explosion has created many possible drug candidates; however, filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an application programming interface or web interface, and has generated 25 high-quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.
Description
Department
Publisher
PeerJ Computer Science
Relationships
https://tw.rpi.edu/project/redrugs
Access