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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6770973 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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