Finding melanoma drugs through a probabilistic knowledge graph

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1 Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York, USA
2 Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, California, United States
3 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, New York, United States
4 Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York, United States
DOI
10.7287/peerj.preprints.2007v2
Published
Accepted
Subject Areas
Bioinformatics, Computational Biology, Data Science, World Wide Web and Web Science
Keywords
melanoma, knowledge graphs, drug repositioning, uncertainty reasoning
Copyright
© 2016 McCusker et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
McCusker JP, Dumontier M, Yan R, He S, Dordick JS, McGuinness DL. 2016. Finding melanoma drugs through a probabilistic knowledge graph. PeerJ Preprints 4:e2007v2

Abstract

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing 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 API 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.

Author Comment

This article has been updated in response to feedback from PeerJ reviewers. The methods and discussion sections have been significantly expanded. This new version has been resubmitted to PeerJ Computer Science.