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A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks

Identifying the interactions between disease and microRNA (miRNA) can accelerate drugs development, individualized diagnosis, and treatment for various human diseases. However, experimental methods are time-consuming and costly. So computational approaches to predict latent miRNA–disease interaction...

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Detalles Bibliográficos
Autores principales: Li, Chunyan, Liu, Hongju, Hu, Qian, Que, Jinlong, Yao, Junfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6769654/
https://www.ncbi.nlm.nih.gov/pubmed/31455028
http://dx.doi.org/10.3390/cells8090977
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author Li, Chunyan
Liu, Hongju
Hu, Qian
Que, Jinlong
Yao, Junfeng
author_facet Li, Chunyan
Liu, Hongju
Hu, Qian
Que, Jinlong
Yao, Junfeng
author_sort Li, Chunyan
collection PubMed
description Identifying the interactions between disease and microRNA (miRNA) can accelerate drugs development, individualized diagnosis, and treatment for various human diseases. However, experimental methods are time-consuming and costly. So computational approaches to predict latent miRNA–disease interactions are eliciting increased attention. But most previous studies have mainly focused on designing complicated similarity-based methods to predict latent interactions between miRNAs and diseases. In this study, we propose a novel computational model, termed heterogeneous graph convolutional network for miRNA–disease associations (HGCNMDA), which is based on known human protein–protein interaction (PPI) and integrates four biological networks: miRNA–disease, miRNA–gene, disease–gene, and PPI network. HGCNMDA achieved reliable performance using leave-one-out cross-validation (LOOCV). HGCNMDA is then compared to three state-of-the-art algorithms based on five-fold cross-validation. HGCNMDA achieves an AUC of 0.9626 and an average precision of 0.9660, respectively, which is ahead of other competitive algorithms. We further analyze the top-10 unknown interactions between miRNA and disease. In summary, HGCNMDA is a useful computational model for predicting miRNA–disease interactions.
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spelling pubmed-67696542019-10-30 A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks Li, Chunyan Liu, Hongju Hu, Qian Que, Jinlong Yao, Junfeng Cells Article Identifying the interactions between disease and microRNA (miRNA) can accelerate drugs development, individualized diagnosis, and treatment for various human diseases. However, experimental methods are time-consuming and costly. So computational approaches to predict latent miRNA–disease interactions are eliciting increased attention. But most previous studies have mainly focused on designing complicated similarity-based methods to predict latent interactions between miRNAs and diseases. In this study, we propose a novel computational model, termed heterogeneous graph convolutional network for miRNA–disease associations (HGCNMDA), which is based on known human protein–protein interaction (PPI) and integrates four biological networks: miRNA–disease, miRNA–gene, disease–gene, and PPI network. HGCNMDA achieved reliable performance using leave-one-out cross-validation (LOOCV). HGCNMDA is then compared to three state-of-the-art algorithms based on five-fold cross-validation. HGCNMDA achieves an AUC of 0.9626 and an average precision of 0.9660, respectively, which is ahead of other competitive algorithms. We further analyze the top-10 unknown interactions between miRNA and disease. In summary, HGCNMDA is a useful computational model for predicting miRNA–disease interactions. MDPI 2019-08-26 /pmc/articles/PMC6769654/ /pubmed/31455028 http://dx.doi.org/10.3390/cells8090977 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
Li, Chunyan
Liu, Hongju
Hu, Qian
Que, Jinlong
Yao, Junfeng
A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks
title A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks
title_full A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks
title_fullStr A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks
title_full_unstemmed A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks
title_short A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks
title_sort novel computational model for predicting microrna–disease associations based on heterogeneous graph convolutional networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6769654/
https://www.ncbi.nlm.nih.gov/pubmed/31455028
http://dx.doi.org/10.3390/cells8090977
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