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A network-based drug repurposing method via non-negative matrix factorization

MOTIVATION: Drug repurposing is a potential alternative to the traditional drug discovery process. Drug repurposing can be formulated as a recommender system that recommends novel indications for available drugs based on known drug-disease associations. This article presents a method based on non-ne...

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Autores principales: Sadeghi, Shaghayegh, Lu, Jianguo, Ngom, Alioune
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825773/
https://www.ncbi.nlm.nih.gov/pubmed/34875000
http://dx.doi.org/10.1093/bioinformatics/btab826
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author Sadeghi, Shaghayegh
Lu, Jianguo
Ngom, Alioune
author_facet Sadeghi, Shaghayegh
Lu, Jianguo
Ngom, Alioune
author_sort Sadeghi, Shaghayegh
collection PubMed
description MOTIVATION: Drug repurposing is a potential alternative to the traditional drug discovery process. Drug repurposing can be formulated as a recommender system that recommends novel indications for available drugs based on known drug-disease associations. This article presents a method based on non-negative matrix factorization (NMF-DR) to predict the drug-related candidate disease indications. This work proposes a recommender system-based method for drug repurposing to predict novel drug indications by integrating drug and diseases related data sources. For this purpose, this framework first integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different views to make a heterogeneous drug–disease interaction network. Then, an improved non-negative matrix factorization-based method is proposed to complete the drug–disease adjacency matrix with predicted scores for unknown drug–disease pairs. RESULTS: The comprehensive experimental results show that NMF-DR achieves superior prediction performance when compared with several existing methods for drug–disease association prediction. AVAILABILITY AND IMPLEMENTATION: The program is available at https://github.com/sshaghayeghs/NMF-DR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-88257732022-02-09 A network-based drug repurposing method via non-negative matrix factorization Sadeghi, Shaghayegh Lu, Jianguo Ngom, Alioune Bioinformatics Original Papers MOTIVATION: Drug repurposing is a potential alternative to the traditional drug discovery process. Drug repurposing can be formulated as a recommender system that recommends novel indications for available drugs based on known drug-disease associations. This article presents a method based on non-negative matrix factorization (NMF-DR) to predict the drug-related candidate disease indications. This work proposes a recommender system-based method for drug repurposing to predict novel drug indications by integrating drug and diseases related data sources. For this purpose, this framework first integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different views to make a heterogeneous drug–disease interaction network. Then, an improved non-negative matrix factorization-based method is proposed to complete the drug–disease adjacency matrix with predicted scores for unknown drug–disease pairs. RESULTS: The comprehensive experimental results show that NMF-DR achieves superior prediction performance when compared with several existing methods for drug–disease association prediction. AVAILABILITY AND IMPLEMENTATION: The program is available at https://github.com/sshaghayeghs/NMF-DR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-12-07 /pmc/articles/PMC8825773/ /pubmed/34875000 http://dx.doi.org/10.1093/bioinformatics/btab826 Text en © The Author(s) 2021. Published by Oxford University Press. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Sadeghi, Shaghayegh
Lu, Jianguo
Ngom, Alioune
A network-based drug repurposing method via non-negative matrix factorization
title A network-based drug repurposing method via non-negative matrix factorization
title_full A network-based drug repurposing method via non-negative matrix factorization
title_fullStr A network-based drug repurposing method via non-negative matrix factorization
title_full_unstemmed A network-based drug repurposing method via non-negative matrix factorization
title_short A network-based drug repurposing method via non-negative matrix factorization
title_sort network-based drug repurposing method via non-negative matrix factorization
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825773/
https://www.ncbi.nlm.nih.gov/pubmed/34875000
http://dx.doi.org/10.1093/bioinformatics/btab826
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