Finding melanoma drugs through a probabilistic knowledge graph

Printer-friendly version

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.

History

DateCreated ByLink
April 6, 2017
14:45:24
John S. EricksonDownload

Related Projects:

Repurposing Drugs with Semantics (ReDrugS)
Principal Investigator: Deborah L. McGuinness and Jonathan Dordick
Description: We aim to find new effective treatments for disease using existing drugs. Our approach is to gather and integrate existing data using semantic technologies to help discover promising drug repurposing.

Related Research Areas:

Health Informatics
Lead Professor: Deborah L. McGuinness
Description:

Health informatics is "the interdisciplinary study of the design, development, adoption and application of IT-based innovations in healthcare services delivery, management and planning." Procter, R. Dr. (Editor, Health Informatics Journal, Edinburgh, United Kingdom). (From the U.S. National Library of Medicine)


Concepts: None.