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Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information

Predicting the potential microRNA (miRNA) candidates associated with a disease helps in exploring the mechanisms of disease development. Most recent approaches have utilized heterogeneous information about miRNAs and diseases, including miRNA similarities, disease similarities, and miRNA-disease ass...

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Detalles Bibliográficos
Autores principales: Xuan, Ping, Zhang, Yan, Zhang, Tiangang, Li, Lingling, Zhao, Lianfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6770973/
https://www.ncbi.nlm.nih.gov/pubmed/31500152
http://dx.doi.org/10.3390/genes10090685
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author Xuan, Ping
Zhang, Yan
Zhang, Tiangang
Li, Lingling
Zhao, Lianfeng
author_facet Xuan, Ping
Zhang, Yan
Zhang, Tiangang
Li, Lingling
Zhao, Lianfeng
author_sort Xuan, Ping
collection PubMed
description Predicting the potential microRNA (miRNA) candidates associated with a disease helps in exploring the mechanisms of disease development. Most recent approaches have utilized heterogeneous information about miRNAs and diseases, including miRNA similarities, disease similarities, and miRNA-disease associations. However, these methods do not utilize the projections of miRNAs and diseases in a low-dimensional space. Thus, it is necessary to develop a method that can utilize the effective information in the low-dimensional space to predict potential disease-related miRNA candidates. We proposed a method based on non-negative matrix factorization, named DMAPred, to predict potential miRNA-disease associations. DMAPred exploits the similarities and associations of diseases and miRNAs, and it integrates local topological information of the miRNA network. The likelihood that a miRNA is associated with a disease also depends on their projections in low-dimensional space. Therefore, we project miRNAs and diseases into low-dimensional feature space to yield their low-dimensional and dense feature representations. Moreover, the sparse characteristic of miRNA-disease associations was introduced to make our predictive model more credible. DMAPred achieved superior performance for 15 well-characterized diseases with AUCs (area under the receiver operating characteristic curve) ranging from 0.860 to 0.973 and AUPRs (area under the precision-recall curve) ranging from 0.118 to 0.761. In addition, case studies on breast, prostatic, and lung neoplasms demonstrated the ability of DMAPred to discover potential disease-related miRNAs.
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spelling pubmed-67709732019-10-30 Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information Xuan, Ping Zhang, Yan Zhang, Tiangang Li, Lingling Zhao, Lianfeng Genes (Basel) Article Predicting the potential microRNA (miRNA) candidates associated with a disease helps in exploring the mechanisms of disease development. Most recent approaches have utilized heterogeneous information about miRNAs and diseases, including miRNA similarities, disease similarities, and miRNA-disease associations. However, these methods do not utilize the projections of miRNAs and diseases in a low-dimensional space. Thus, it is necessary to develop a method that can utilize the effective information in the low-dimensional space to predict potential disease-related miRNA candidates. We proposed a method based on non-negative matrix factorization, named DMAPred, to predict potential miRNA-disease associations. DMAPred exploits the similarities and associations of diseases and miRNAs, and it integrates local topological information of the miRNA network. The likelihood that a miRNA is associated with a disease also depends on their projections in low-dimensional space. Therefore, we project miRNAs and diseases into low-dimensional feature space to yield their low-dimensional and dense feature representations. Moreover, the sparse characteristic of miRNA-disease associations was introduced to make our predictive model more credible. DMAPred achieved superior performance for 15 well-characterized diseases with AUCs (area under the receiver operating characteristic curve) ranging from 0.860 to 0.973 and AUPRs (area under the precision-recall curve) ranging from 0.118 to 0.761. In addition, case studies on breast, prostatic, and lung neoplasms demonstrated the ability of DMAPred to discover potential disease-related miRNAs. MDPI 2019-09-06 /pmc/articles/PMC6770973/ /pubmed/31500152 http://dx.doi.org/10.3390/genes10090685 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xuan, Ping
Zhang, Yan
Zhang, Tiangang
Li, Lingling
Zhao, Lianfeng
Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information
title Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information
title_full Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information
title_fullStr Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information
title_full_unstemmed Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information
title_short Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information
title_sort predicting mirna-disease associations by incorporating projections in low-dimensional space and local topological information
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6770973/
https://www.ncbi.nlm.nih.gov/pubmed/31500152
http://dx.doi.org/10.3390/genes10090685
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