Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1783455288159895552 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6769654 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lichunyan anovelcomputationalmodelforpredictingmicrornadiseaseassociationsbasedonheterogeneousgraphconvolutionalnetworks AT liuhongju anovelcomputationalmodelforpredictingmicrornadiseaseassociationsbasedonheterogeneousgraphconvolutionalnetworks AT huqian anovelcomputationalmodelforpredictingmicrornadiseaseassociationsbasedonheterogeneousgraphconvolutionalnetworks AT quejinlong anovelcomputationalmodelforpredictingmicrornadiseaseassociationsbasedonheterogeneousgraphconvolutionalnetworks AT yaojunfeng anovelcomputationalmodelforpredictingmicrornadiseaseassociationsbasedonheterogeneousgraphconvolutionalnetworks AT lichunyan novelcomputationalmodelforpredictingmicrornadiseaseassociationsbasedonheterogeneousgraphconvolutionalnetworks AT liuhongju novelcomputationalmodelforpredictingmicrornadiseaseassociationsbasedonheterogeneousgraphconvolutionalnetworks AT huqian novelcomputationalmodelforpredictingmicrornadiseaseassociationsbasedonheterogeneousgraphconvolutionalnetworks AT quejinlong novelcomputationalmodelforpredictingmicrornadiseaseassociationsbasedonheterogeneousgraphconvolutionalnetworks AT yaojunfeng novelcomputationalmodelforpredictingmicrornadiseaseassociationsbasedonheterogeneousgraphconvolutionalnetworks |