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Prediction of miRNA-disease associations with a vector space model
MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of...
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/PMC4887905/ https://www.ncbi.nlm.nih.gov/pubmed/27246786 http://dx.doi.org/10.1038/srep27036 |
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author | Pasquier, Claude Gardès, Julien |
author_facet | Pasquier, Claude Gardès, Julien |
author_sort | Pasquier, Claude |
collection | PubMed |
description | MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases. |
format | Online Article Text |
id | pubmed-4887905 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-48879052016-06-09 Prediction of miRNA-disease associations with a vector space model Pasquier, Claude Gardès, Julien Sci Rep Article MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases. Nature Publishing Group 2016-06-01 /pmc/articles/PMC4887905/ /pubmed/27246786 http://dx.doi.org/10.1038/srep27036 Text en Copyright © 2016, Macmillan Publishers Limited 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 Pasquier, Claude Gardès, Julien Prediction of miRNA-disease associations with a vector space model |
title | Prediction of miRNA-disease associations with a vector space model |
title_full | Prediction of miRNA-disease associations with a vector space model |
title_fullStr | Prediction of miRNA-disease associations with a vector space model |
title_full_unstemmed | Prediction of miRNA-disease associations with a vector space model |
title_short | Prediction of miRNA-disease associations with a vector space model |
title_sort | prediction of mirna-disease associations with a vector space model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4887905/ https://www.ncbi.nlm.nih.gov/pubmed/27246786 http://dx.doi.org/10.1038/srep27036 |
work_keys_str_mv | AT pasquierclaude predictionofmirnadiseaseassociationswithavectorspacemodel AT gardesjulien predictionofmirnadiseaseassociationswithavectorspacemodel |