CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

Abstract Background The existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to...

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Autores principales:	Zhihao Ma, Zhufang Kuang, Lei Deng
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	CircRNA-disease Graph convolutional network Heterogenous network Principal component analysis Deep learning Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/678b5d4784a644f3ba2c6544c11456f4
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spelling	oai:doaj.org-article:678b5d4784a644f3ba2c6544c11456f42021-11-14T12:13:16ZCRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network10.1186/s12859-021-04467-z1471-2105https://doaj.org/article/678b5d4784a644f3ba2c6544c11456f42021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04467-zhttps://doaj.org/toc/1471-2105Abstract Background The existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to study the mechanism of circRNAs. Nowadays, some scholars use the attributes between circRNAs and diseases to study and predict their associations. Nonetheless, most of the existing experimental methods use less information about the attributes of circRNAs, which has a certain impact on the accuracy of the final prediction results. On the other hand, some scholars also apply experimental methods to predict the associations between circRNAs and diseases. But such methods are usually expensive and time-consuming. Based on the above shortcomings, follow-up research is needed to propose a more efficient calculation-based method to predict the associations between circRNAs and diseases. Results In this study, a novel algorithm (method) is proposed, which is based on the Graph Convolutional Network (GCN) constructed with Random Walk with Restart (RWR) and Principal Component Analysis (PCA) to predict the associations between circRNAs and diseases (CRPGCN). In the construction of CRPGCN, the RWR algorithm is used to improve the similarity associations of the computed nodes with their neighbours. After that, the PCA method is used to dimensionality reduction and extract features, it makes the connection between circRNAs with higher similarity and diseases closer. Finally, The GCN algorithm is used to learn the features between circRNAs and diseases and calculate the final similarity scores, and the learning datas are constructed from the adjacency matrix, similarity matrix and feature matrix as a heterogeneous adjacency matrix and a heterogeneous feature matrix. Conclusions After 2-fold cross-validation, 5-fold cross-validation and 10-fold cross-validation, the area under the ROC curve of the CRPGCN is 0.9490, 0.9720 and 0.9722, respectively. The CRPGCN method has a valuable effect in predict the associations between circRNAs and diseases.Zhihao MaZhufang KuangLei DengBMCarticleCircRNA-diseaseGraph convolutional networkHeterogenous networkPrincipal component analysisDeep learningComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-23 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CircRNA-disease Graph convolutional network Heterogenous network Principal component analysis Deep learning Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	CircRNA-disease Graph convolutional network Heterogenous network Principal component analysis Deep learning Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Zhihao Ma Zhufang Kuang Lei Deng CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network
description	Abstract Background The existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to study the mechanism of circRNAs. Nowadays, some scholars use the attributes between circRNAs and diseases to study and predict their associations. Nonetheless, most of the existing experimental methods use less information about the attributes of circRNAs, which has a certain impact on the accuracy of the final prediction results. On the other hand, some scholars also apply experimental methods to predict the associations between circRNAs and diseases. But such methods are usually expensive and time-consuming. Based on the above shortcomings, follow-up research is needed to propose a more efficient calculation-based method to predict the associations between circRNAs and diseases. Results In this study, a novel algorithm (method) is proposed, which is based on the Graph Convolutional Network (GCN) constructed with Random Walk with Restart (RWR) and Principal Component Analysis (PCA) to predict the associations between circRNAs and diseases (CRPGCN). In the construction of CRPGCN, the RWR algorithm is used to improve the similarity associations of the computed nodes with their neighbours. After that, the PCA method is used to dimensionality reduction and extract features, it makes the connection between circRNAs with higher similarity and diseases closer. Finally, The GCN algorithm is used to learn the features between circRNAs and diseases and calculate the final similarity scores, and the learning datas are constructed from the adjacency matrix, similarity matrix and feature matrix as a heterogeneous adjacency matrix and a heterogeneous feature matrix. Conclusions After 2-fold cross-validation, 5-fold cross-validation and 10-fold cross-validation, the area under the ROC curve of the CRPGCN is 0.9490, 0.9720 and 0.9722, respectively. The CRPGCN method has a valuable effect in predict the associations between circRNAs and diseases.
format	article
author	Zhihao Ma Zhufang Kuang Lei Deng
author_facet	Zhihao Ma Zhufang Kuang Lei Deng
author_sort	Zhihao Ma
title	CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network
title_short	CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network
title_full	CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network
title_fullStr	CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network
title_full_unstemmed	CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network
title_sort	crpgcn: predicting circrna-disease associations using graph convolutional network based on heterogeneous network
publisher	BMC
publishDate	2021
url	https://doaj.org/article/678b5d4784a644f3ba2c6544c11456f4
work_keys_str_mv	AT zhihaoma crpgcnpredictingcircrnadiseaseassociationsusinggraphconvolutionalnetworkbasedonheterogeneousnetwork AT zhufangkuang crpgcnpredictingcircrnadiseaseassociationsusinggraphconvolutionalnetworkbasedonheterogeneousnetwork AT leideng crpgcnpredictingcircrnadiseaseassociationsusinggraphconvolutionalnetworkbasedonheterogeneousnetwork
_version_	1718429350465896448

CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

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