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Finding melanoma drugs through a probabilistic knowledge graph
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 d...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10151034/ https://www.ncbi.nlm.nih.gov/pubmed/37133296 http://dx.doi.org/10.7717/peerj-cs.106 |
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author | McCusker, Jamie Patricia Dumontier, Michel Yan, Rui He, Sylvia Dordick, Jonathan S. McGuinness, Deborah L. |
author_facet | McCusker, Jamie Patricia Dumontier, Michel Yan, Rui He, Sylvia Dordick, Jonathan S. McGuinness, Deborah L. |
author_sort | McCusker, Jamie Patricia |
collection | PubMed |
description | 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 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. |
format | Online Article Text |
id | pubmed-10151034 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101510342023-05-02 Finding melanoma drugs through a probabilistic knowledge graph McCusker, Jamie Patricia Dumontier, Michel Yan, Rui He, Sylvia Dordick, Jonathan S. McGuinness, Deborah L. PeerJ Comput Sci Bioinformatics 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 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. PeerJ Inc. 2017-02-13 /pmc/articles/PMC10151034/ /pubmed/37133296 http://dx.doi.org/10.7717/peerj-cs.106 Text en © 2017 McCusker et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , 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 Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics McCusker, Jamie Patricia Dumontier, Michel Yan, Rui He, Sylvia Dordick, Jonathan S. McGuinness, Deborah L. Finding melanoma drugs through a probabilistic knowledge graph |
title | Finding melanoma drugs through a probabilistic knowledge graph |
title_full | Finding melanoma drugs through a probabilistic knowledge graph |
title_fullStr | Finding melanoma drugs through a probabilistic knowledge graph |
title_full_unstemmed | Finding melanoma drugs through a probabilistic knowledge graph |
title_short | Finding melanoma drugs through a probabilistic knowledge graph |
title_sort | finding melanoma drugs through a probabilistic knowledge graph |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10151034/ https://www.ncbi.nlm.nih.gov/pubmed/37133296 http://dx.doi.org/10.7717/peerj-cs.106 |
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