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Predicting MiRNA-Disease Association by Latent Feature Extraction with Positive Samples
In discovering disease etiology and pathogenesis, the associations between MicroRNAs (miRNAs) and diseases play a critical role. Given known miRNA-disease associations (MDAs), how to uncover potential MDAs is an important problem. To solve this problem, most of the existing methods regard known MDAs...
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/PMC6410147/ https://www.ncbi.nlm.nih.gov/pubmed/30682853 http://dx.doi.org/10.3390/genes10020080 |
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author | Che, Kai Guo, Maozu Wang, Chunyu Liu, Xiaoyan Chen, Xi |
author_facet | Che, Kai Guo, Maozu Wang, Chunyu Liu, Xiaoyan Chen, Xi |
author_sort | Che, Kai |
collection | PubMed |
description | In discovering disease etiology and pathogenesis, the associations between MicroRNAs (miRNAs) and diseases play a critical role. Given known miRNA-disease associations (MDAs), how to uncover potential MDAs is an important problem. To solve this problem, most of the existing methods regard known MDAs as positive samples and unknown ones as negative samples, and then predict possible MDAs by iteratively revising the negative samples. However, simply viewing unknown MDAs as negative samples introduces erroneous information, which may result in poor predication performance. To avoid such defects, we present a novel method using only positive samples to predict MDAs by latent features extraction (LFEMDA). We design a new approach to construct the miRNAs similarity matrix. LFEMDA integrates the disease similarity matrix, the known MDAs and the miRNAs similarity matrix to identify potential MDAs. By introducing miRNAs and diseases knowledge as the auxiliary variables, the method can converge to give the optimal solution in each iteration. We conduct experiments on high-association diseases and new diseases datasets, in which our method shows better performance than that of other methods. We also carry out a case study on breast neoplasms to further demonstrate the capacity of our method in uncovering potential MDAs. |
format | Online Article Text |
id | pubmed-6410147 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64101472019-03-26 Predicting MiRNA-Disease Association by Latent Feature Extraction with Positive Samples Che, Kai Guo, Maozu Wang, Chunyu Liu, Xiaoyan Chen, Xi Genes (Basel) Article In discovering disease etiology and pathogenesis, the associations between MicroRNAs (miRNAs) and diseases play a critical role. Given known miRNA-disease associations (MDAs), how to uncover potential MDAs is an important problem. To solve this problem, most of the existing methods regard known MDAs as positive samples and unknown ones as negative samples, and then predict possible MDAs by iteratively revising the negative samples. However, simply viewing unknown MDAs as negative samples introduces erroneous information, which may result in poor predication performance. To avoid such defects, we present a novel method using only positive samples to predict MDAs by latent features extraction (LFEMDA). We design a new approach to construct the miRNAs similarity matrix. LFEMDA integrates the disease similarity matrix, the known MDAs and the miRNAs similarity matrix to identify potential MDAs. By introducing miRNAs and diseases knowledge as the auxiliary variables, the method can converge to give the optimal solution in each iteration. We conduct experiments on high-association diseases and new diseases datasets, in which our method shows better performance than that of other methods. We also carry out a case study on breast neoplasms to further demonstrate the capacity of our method in uncovering potential MDAs. MDPI 2019-01-24 /pmc/articles/PMC6410147/ /pubmed/30682853 http://dx.doi.org/10.3390/genes10020080 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 Che, Kai Guo, Maozu Wang, Chunyu Liu, Xiaoyan Chen, Xi Predicting MiRNA-Disease Association by Latent Feature Extraction with Positive Samples |
title | Predicting MiRNA-Disease Association by Latent Feature Extraction with Positive Samples |
title_full | Predicting MiRNA-Disease Association by Latent Feature Extraction with Positive Samples |
title_fullStr | Predicting MiRNA-Disease Association by Latent Feature Extraction with Positive Samples |
title_full_unstemmed | Predicting MiRNA-Disease Association by Latent Feature Extraction with Positive Samples |
title_short | Predicting MiRNA-Disease Association by Latent Feature Extraction with Positive Samples |
title_sort | predicting mirna-disease association by latent feature extraction with positive samples |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6410147/ https://www.ncbi.nlm.nih.gov/pubmed/30682853 http://dx.doi.org/10.3390/genes10020080 |
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