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Computational drug repositioning through heterogeneous network clustering
BACKGROUND: Given the costly and time consuming process and high attrition rates in drug discovery and development, drug repositioning or drug repurposing is considered as a viable strategy both to replenish the drying out drug pipelines and to surmount the innovation gap. Although there is a growin...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4029299/ https://www.ncbi.nlm.nih.gov/pubmed/24564976 http://dx.doi.org/10.1186/1752-0509-7-S5-S6 |
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author | Wu, Chao Gudivada, Ranga C Aronow, Bruce J Jegga, Anil G |
author_facet | Wu, Chao Gudivada, Ranga C Aronow, Bruce J Jegga, Anil G |
author_sort | Wu, Chao |
collection | PubMed |
description | BACKGROUND: Given the costly and time consuming process and high attrition rates in drug discovery and development, drug repositioning or drug repurposing is considered as a viable strategy both to replenish the drying out drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational drug-repositioning candidate discovery platforms. RESULTS: Using known disease-gene and drug-target relationships from the KEGG database, we built a weighted disease and drug heterogeneous network. The nodes represent drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible drug-disease pairs (putative drug repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with drug indications that were either reported in published literature or investigated in clinical trials. CONCLUSIONS: Previous computational approaches for drug repositioning focused either on drug-drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering drug-disease relationships also. Further, we considered not only gene but also other features to build the disease drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with drug indications that are under investigation, we believe our approach could complement the current computational approaches for drug repositioning candidate discovery. |
format | Online Article Text |
id | pubmed-4029299 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-40292992014-06-19 Computational drug repositioning through heterogeneous network clustering Wu, Chao Gudivada, Ranga C Aronow, Bruce J Jegga, Anil G BMC Syst Biol Research BACKGROUND: Given the costly and time consuming process and high attrition rates in drug discovery and development, drug repositioning or drug repurposing is considered as a viable strategy both to replenish the drying out drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational drug-repositioning candidate discovery platforms. RESULTS: Using known disease-gene and drug-target relationships from the KEGG database, we built a weighted disease and drug heterogeneous network. The nodes represent drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible drug-disease pairs (putative drug repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with drug indications that were either reported in published literature or investigated in clinical trials. CONCLUSIONS: Previous computational approaches for drug repositioning focused either on drug-drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering drug-disease relationships also. Further, we considered not only gene but also other features to build the disease drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with drug indications that are under investigation, we believe our approach could complement the current computational approaches for drug repositioning candidate discovery. BioMed Central 2013-12-09 /pmc/articles/PMC4029299/ /pubmed/24564976 http://dx.doi.org/10.1186/1752-0509-7-S5-S6 Text en Copyright © 2013 Wu et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Wu, Chao Gudivada, Ranga C Aronow, Bruce J Jegga, Anil G Computational drug repositioning through heterogeneous network clustering |
title | Computational drug repositioning through heterogeneous network clustering |
title_full | Computational drug repositioning through heterogeneous network clustering |
title_fullStr | Computational drug repositioning through heterogeneous network clustering |
title_full_unstemmed | Computational drug repositioning through heterogeneous network clustering |
title_short | Computational drug repositioning through heterogeneous network clustering |
title_sort | computational drug repositioning through heterogeneous network clustering |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4029299/ https://www.ncbi.nlm.nih.gov/pubmed/24564976 http://dx.doi.org/10.1186/1752-0509-7-S5-S6 |
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