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Prediction of new drug indications based on clinical data and network modularity
Drug repositioning is commonly done within the drug discovery process in order to adjust or expand the application line of an active molecule. Previous computational methods in this domain mainly focused on shared genes or correlations between genes to construct new drug-disease associations. We pro...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5039412/ https://www.ncbi.nlm.nih.gov/pubmed/27678071 http://dx.doi.org/10.1038/srep32530 |
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author | Yu, Liang Ma, Xiaoke Zhang, Long Zhang, Jing Gao, Lin |
author_facet | Yu, Liang Ma, Xiaoke Zhang, Long Zhang, Jing Gao, Lin |
author_sort | Yu, Liang |
collection | PubMed |
description | Drug repositioning is commonly done within the drug discovery process in order to adjust or expand the application line of an active molecule. Previous computational methods in this domain mainly focused on shared genes or correlations between genes to construct new drug-disease associations. We propose a method that can not only handle drugs or diseases with or without related genes but consider the network modularity. Our method firstly constructs a drug network and a disease network based on side effects and symptoms respectively. Because similar drugs imply similar diseases, we then cluster the two networks to identify drug and disease modules, and connect all possible drug-disease module pairs. Further, based on known drug-disease associations in CTD and using local connectivity of modules, we predict potential drug-disease associations. Our predictions are validated by testing their overlaps with drug indications reported in published literatures and CTD, and KEGG enrichment analysis are also made on their related genes. The experimental results demonstrate that our approach can complement the current computational approaches and its predictions can provide new clues for the candidate discovery of drug repositioning. |
format | Online Article Text |
id | pubmed-5039412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-50394122016-09-30 Prediction of new drug indications based on clinical data and network modularity Yu, Liang Ma, Xiaoke Zhang, Long Zhang, Jing Gao, Lin Sci Rep Article Drug repositioning is commonly done within the drug discovery process in order to adjust or expand the application line of an active molecule. Previous computational methods in this domain mainly focused on shared genes or correlations between genes to construct new drug-disease associations. We propose a method that can not only handle drugs or diseases with or without related genes but consider the network modularity. Our method firstly constructs a drug network and a disease network based on side effects and symptoms respectively. Because similar drugs imply similar diseases, we then cluster the two networks to identify drug and disease modules, and connect all possible drug-disease module pairs. Further, based on known drug-disease associations in CTD and using local connectivity of modules, we predict potential drug-disease associations. Our predictions are validated by testing their overlaps with drug indications reported in published literatures and CTD, and KEGG enrichment analysis are also made on their related genes. The experimental results demonstrate that our approach can complement the current computational approaches and its predictions can provide new clues for the candidate discovery of drug repositioning. Nature Publishing Group 2016-09-28 /pmc/articles/PMC5039412/ /pubmed/27678071 http://dx.doi.org/10.1038/srep32530 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Yu, Liang Ma, Xiaoke Zhang, Long Zhang, Jing Gao, Lin Prediction of new drug indications based on clinical data and network modularity |
title | Prediction of new drug indications based on clinical data and network modularity |
title_full | Prediction of new drug indications based on clinical data and network modularity |
title_fullStr | Prediction of new drug indications based on clinical data and network modularity |
title_full_unstemmed | Prediction of new drug indications based on clinical data and network modularity |
title_short | Prediction of new drug indications based on clinical data and network modularity |
title_sort | prediction of new drug indications based on clinical data and network modularity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5039412/ https://www.ncbi.nlm.nih.gov/pubmed/27678071 http://dx.doi.org/10.1038/srep32530 |
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