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Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding
MicroRNAs (miRNAs) are a category of small non-coding RNAs that profoundly impact various biological processes related to human disease. Inferring the potential miRNA-disease associations benefits the study of human diseases, such as disease prevention, disease diagnosis, and drug development. In th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223753/ https://www.ncbi.nlm.nih.gov/pubmed/34178973 http://dx.doi.org/10.3389/fcell.2021.603758 |
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author | Peng, Wei Du, Jielin Dai, Wei Lan, Wei |
author_facet | Peng, Wei Du, Jielin Dai, Wei Lan, Wei |
author_sort | Peng, Wei |
collection | PubMed |
description | MicroRNAs (miRNAs) are a category of small non-coding RNAs that profoundly impact various biological processes related to human disease. Inferring the potential miRNA-disease associations benefits the study of human diseases, such as disease prevention, disease diagnosis, and drug development. In this work, we propose a novel heterogeneous network embedding-based method called MDN-NMTF (Module-based Dynamic Neighborhood Non-negative Matrix Tri-Factorization) for predicting miRNA-disease associations. MDN-NMTF constructs a heterogeneous network of disease similarity network, miRNA similarity network and a known miRNA-disease association network. After that, it learns the latent vector representation for miRNAs and diseases in the heterogeneous network. Finally, the association probability is computed by the product of the latent miRNA and disease vectors. MDN-NMTF not only successfully integrates diverse biological information of miRNAs and diseases to predict miRNA-disease associations, but also considers the module properties of miRNAs and diseases in the course of learning vector representation, which can maximally preserve the heterogeneous network structural information and the network properties. At the same time, we also extend MDN-NMTF to a new version (called MDN-NMTF2) by using modular information to improve the miRNA-disease association prediction ability. Our methods and the other four existing methods are applied to predict miRNA-disease associations in four databases. The prediction results show that our methods can improve the miRNA-disease association prediction to a high level compared with the four existing methods. |
format | Online Article Text |
id | pubmed-8223753 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82237532021-06-25 Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding Peng, Wei Du, Jielin Dai, Wei Lan, Wei Front Cell Dev Biol Cell and Developmental Biology MicroRNAs (miRNAs) are a category of small non-coding RNAs that profoundly impact various biological processes related to human disease. Inferring the potential miRNA-disease associations benefits the study of human diseases, such as disease prevention, disease diagnosis, and drug development. In this work, we propose a novel heterogeneous network embedding-based method called MDN-NMTF (Module-based Dynamic Neighborhood Non-negative Matrix Tri-Factorization) for predicting miRNA-disease associations. MDN-NMTF constructs a heterogeneous network of disease similarity network, miRNA similarity network and a known miRNA-disease association network. After that, it learns the latent vector representation for miRNAs and diseases in the heterogeneous network. Finally, the association probability is computed by the product of the latent miRNA and disease vectors. MDN-NMTF not only successfully integrates diverse biological information of miRNAs and diseases to predict miRNA-disease associations, but also considers the module properties of miRNAs and diseases in the course of learning vector representation, which can maximally preserve the heterogeneous network structural information and the network properties. At the same time, we also extend MDN-NMTF to a new version (called MDN-NMTF2) by using modular information to improve the miRNA-disease association prediction ability. Our methods and the other four existing methods are applied to predict miRNA-disease associations in four databases. The prediction results show that our methods can improve the miRNA-disease association prediction to a high level compared with the four existing methods. Frontiers Media S.A. 2021-06-10 /pmc/articles/PMC8223753/ /pubmed/34178973 http://dx.doi.org/10.3389/fcell.2021.603758 Text en Copyright © 2021 Peng, Du, Dai and Lan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cell and Developmental Biology Peng, Wei Du, Jielin Dai, Wei Lan, Wei Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding |
title | Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding |
title_full | Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding |
title_fullStr | Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding |
title_full_unstemmed | Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding |
title_short | Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding |
title_sort | predicting mirna-disease association based on modularity preserving heterogeneous network embedding |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223753/ https://www.ncbi.nlm.nih.gov/pubmed/34178973 http://dx.doi.org/10.3389/fcell.2021.603758 |
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