Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Peng, Wei, Du, Jielin, Dai, Wei, Lan, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
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
_version_ 1783711756017729536
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
work_keys_str_mv AT pengwei predictingmirnadiseaseassociationbasedonmodularitypreservingheterogeneousnetworkembedding
AT dujielin predictingmirnadiseaseassociationbasedonmodularitypreservingheterogeneousnetworkembedding
AT daiwei predictingmirnadiseaseassociationbasedonmodularitypreservingheterogeneousnetworkembedding
AT lanwei predictingmirnadiseaseassociationbasedonmodularitypreservingheterogeneousnetworkembedding