Cargando…
Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network
Circular RNAs (circRNAs) are covalently closed single-stranded RNA molecules, which have many biological functions. Previous experiments have shown that circRNAs are involved in numerous biological processes, especially regulatory functions. It has also been found that circRNAs are associated with c...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313348/ https://www.ncbi.nlm.nih.gov/pubmed/35883487 http://dx.doi.org/10.3390/biom12070932 |
_version_ | 1784754058238623744 |
---|---|
author | Cao, Ruifen He, Chuan Wei, Pijing Su, Yansen Xia, Junfeng Zheng, Chunhou |
author_facet | Cao, Ruifen He, Chuan Wei, Pijing Su, Yansen Xia, Junfeng Zheng, Chunhou |
author_sort | Cao, Ruifen |
collection | PubMed |
description | Circular RNAs (circRNAs) are covalently closed single-stranded RNA molecules, which have many biological functions. Previous experiments have shown that circRNAs are involved in numerous biological processes, especially regulatory functions. It has also been found that circRNAs are associated with complex diseases of human beings. Therefore, predicting the associations of circRNA with disease (called circRNA-disease associations) is useful for disease prevention, diagnosis and treatment. In this work, we propose a novel computational approach called GGCDA based on the Graph Attention Network (GAT) and Graph Convolutional Network (GCN) to predict circRNA-disease associations. Firstly, GGCDA combines circRNA sequence similarity, disease semantic similarity and corresponding Gaussian interaction profile kernel similarity, and then a random walk with restart algorithm (RWR) is used to obtain the preliminary features of circRNA and disease. Secondly, a heterogeneous graph is constructed from the known circRNA-disease association network and the calculated similarity of circRNAs and diseases. Thirdly, the multi-head Graph Attention Network (GAT) is adopted to obtain different weights of circRNA and disease features, and then GCN is employed to aggregate the features of adjacent nodes in the network and the features of the nodes themselves, so as to obtain multi-view circRNA and disease features. Finally, we combined a multi-layer fully connected neural network to predict the associations of circRNAs with diseases. In comparison with state-of-the-art methods, GGCDA can achieve AUC values of 0.9625 and 0.9485 under the results of fivefold cross-validation on two datasets, and AUC of 0.8227 on the independent test set. Case studies further demonstrate that our approach is promising for discovering potential circRNA-disease associations. |
format | Online Article Text |
id | pubmed-9313348 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93133482022-07-26 Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network Cao, Ruifen He, Chuan Wei, Pijing Su, Yansen Xia, Junfeng Zheng, Chunhou Biomolecules Article Circular RNAs (circRNAs) are covalently closed single-stranded RNA molecules, which have many biological functions. Previous experiments have shown that circRNAs are involved in numerous biological processes, especially regulatory functions. It has also been found that circRNAs are associated with complex diseases of human beings. Therefore, predicting the associations of circRNA with disease (called circRNA-disease associations) is useful for disease prevention, diagnosis and treatment. In this work, we propose a novel computational approach called GGCDA based on the Graph Attention Network (GAT) and Graph Convolutional Network (GCN) to predict circRNA-disease associations. Firstly, GGCDA combines circRNA sequence similarity, disease semantic similarity and corresponding Gaussian interaction profile kernel similarity, and then a random walk with restart algorithm (RWR) is used to obtain the preliminary features of circRNA and disease. Secondly, a heterogeneous graph is constructed from the known circRNA-disease association network and the calculated similarity of circRNAs and diseases. Thirdly, the multi-head Graph Attention Network (GAT) is adopted to obtain different weights of circRNA and disease features, and then GCN is employed to aggregate the features of adjacent nodes in the network and the features of the nodes themselves, so as to obtain multi-view circRNA and disease features. Finally, we combined a multi-layer fully connected neural network to predict the associations of circRNAs with diseases. In comparison with state-of-the-art methods, GGCDA can achieve AUC values of 0.9625 and 0.9485 under the results of fivefold cross-validation on two datasets, and AUC of 0.8227 on the independent test set. Case studies further demonstrate that our approach is promising for discovering potential circRNA-disease associations. MDPI 2022-07-02 /pmc/articles/PMC9313348/ /pubmed/35883487 http://dx.doi.org/10.3390/biom12070932 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cao, Ruifen He, Chuan Wei, Pijing Su, Yansen Xia, Junfeng Zheng, Chunhou Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network |
title | Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network |
title_full | Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network |
title_fullStr | Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network |
title_full_unstemmed | Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network |
title_short | Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network |
title_sort | prediction of circrna-disease associations based on the combination of multi-head graph attention network and graph convolutional network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313348/ https://www.ncbi.nlm.nih.gov/pubmed/35883487 http://dx.doi.org/10.3390/biom12070932 |
work_keys_str_mv | AT caoruifen predictionofcircrnadiseaseassociationsbasedonthecombinationofmultiheadgraphattentionnetworkandgraphconvolutionalnetwork AT hechuan predictionofcircrnadiseaseassociationsbasedonthecombinationofmultiheadgraphattentionnetworkandgraphconvolutionalnetwork AT weipijing predictionofcircrnadiseaseassociationsbasedonthecombinationofmultiheadgraphattentionnetworkandgraphconvolutionalnetwork AT suyansen predictionofcircrnadiseaseassociationsbasedonthecombinationofmultiheadgraphattentionnetworkandgraphconvolutionalnetwork AT xiajunfeng predictionofcircrnadiseaseassociationsbasedonthecombinationofmultiheadgraphattentionnetworkandgraphconvolutionalnetwork AT zhengchunhou predictionofcircrnadiseaseassociationsbasedonthecombinationofmultiheadgraphattentionnetworkandgraphconvolutionalnetwork |