EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

Abstract Background A large number of biological studies have shown that miRNAs are inextricably linked to many complex diseases. Studying the miRNA-disease associations could provide us a root cause understanding of the underlying pathogenesis in which promotes the progress of drug development. How...

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Autores principales:	Shanchen Pang, Yu Zhuang, Xinzeng Wang, Fuyu Wang, Sibo Qiao
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	miRNA-disease associations Embedding of embedding Simplified graph convolutional network Coupled heterogeneous graph Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/7478900349554ad7bdfbdd107802fd92
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spelling	oai:doaj.org-article:7478900349554ad7bdfbdd107802fd922021-11-21T12:28:58ZEOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network10.1186/s12911-021-01671-y1472-6947https://doaj.org/article/7478900349554ad7bdfbdd107802fd922021-11-01T00:00:00Zhttps://doi.org/10.1186/s12911-021-01671-yhttps://doaj.org/toc/1472-6947Abstract Background A large number of biological studies have shown that miRNAs are inextricably linked to many complex diseases. Studying the miRNA-disease associations could provide us a root cause understanding of the underlying pathogenesis in which promotes the progress of drug development. However, traditional biological experiments are very time-consuming and costly. Therefore, we come up with an efficient models to solve this challenge. Results In this work, we propose a deep learning model called EOESGC to predict potential miRNA-disease associations based on embedding of embedding and simplified convolutional network. Firstly, integrated disease similarity, integrated miRNA similarity, and miRNA-disease association network are used to construct a coupled heterogeneous graph, and the edges with low similarity are removed to simplify the graph structure and ensure the effectiveness of edges. Secondly, the Embedding of embedding model (EOE) is used to learn edge information in the coupled heterogeneous graph. The training rule of the model is that the associated nodes are close to each other and the unassociated nodes are far away from each other. Based on this rule, edge information learned is added into node embedding as supplementary information to enrich node information. Then, node embedding of EOE model training as a new feature of miRNA and disease, and information aggregation is performed by simplified graph convolution model, in which each level of convolution can aggregate multi-hop neighbor information. In this step, we only use the miRNA-disease association network to further simplify the graph structure, thus reducing the computational complexity. Finally, feature embeddings of both miRNA and disease are spliced into the MLP for prediction. On the EOESGC evaluation part, the AUC, AUPR, and F1-score of our model are 0.9658, 0.8543 and 0.8644 by 5-fold cross-validation respectively. Compared with the latest published models, our model shows better results. In addition, we predict the top 20 potential miRNAs for breast cancer and lung cancer, most of which are validated in the dbDEMC and HMDD3.2 databases. Conclusion The comprehensive experimental results show that EOESGC can effectively identify the potential miRNA-disease associations.Shanchen PangYu ZhuangXinzeng WangFuyu WangSibo QiaoBMCarticlemiRNA-disease associationsEmbedding of embeddingSimplified graph convolutional networkCoupled heterogeneous graphComputer applications to medicine. Medical informaticsR858-859.7ENBMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	miRNA-disease associations Embedding of embedding Simplified graph convolutional network Coupled heterogeneous graph Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	miRNA-disease associations Embedding of embedding Simplified graph convolutional network Coupled heterogeneous graph Computer applications to medicine. Medical informatics R858-859.7 Shanchen Pang Yu Zhuang Xinzeng Wang Fuyu Wang Sibo Qiao EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network
description	Abstract Background A large number of biological studies have shown that miRNAs are inextricably linked to many complex diseases. Studying the miRNA-disease associations could provide us a root cause understanding of the underlying pathogenesis in which promotes the progress of drug development. However, traditional biological experiments are very time-consuming and costly. Therefore, we come up with an efficient models to solve this challenge. Results In this work, we propose a deep learning model called EOESGC to predict potential miRNA-disease associations based on embedding of embedding and simplified convolutional network. Firstly, integrated disease similarity, integrated miRNA similarity, and miRNA-disease association network are used to construct a coupled heterogeneous graph, and the edges with low similarity are removed to simplify the graph structure and ensure the effectiveness of edges. Secondly, the Embedding of embedding model (EOE) is used to learn edge information in the coupled heterogeneous graph. The training rule of the model is that the associated nodes are close to each other and the unassociated nodes are far away from each other. Based on this rule, edge information learned is added into node embedding as supplementary information to enrich node information. Then, node embedding of EOE model training as a new feature of miRNA and disease, and information aggregation is performed by simplified graph convolution model, in which each level of convolution can aggregate multi-hop neighbor information. In this step, we only use the miRNA-disease association network to further simplify the graph structure, thus reducing the computational complexity. Finally, feature embeddings of both miRNA and disease are spliced into the MLP for prediction. On the EOESGC evaluation part, the AUC, AUPR, and F1-score of our model are 0.9658, 0.8543 and 0.8644 by 5-fold cross-validation respectively. Compared with the latest published models, our model shows better results. In addition, we predict the top 20 potential miRNAs for breast cancer and lung cancer, most of which are validated in the dbDEMC and HMDD3.2 databases. Conclusion The comprehensive experimental results show that EOESGC can effectively identify the potential miRNA-disease associations.
format	article
author	Shanchen Pang Yu Zhuang Xinzeng Wang Fuyu Wang Sibo Qiao
author_facet	Shanchen Pang Yu Zhuang Xinzeng Wang Fuyu Wang Sibo Qiao
author_sort	Shanchen Pang
title	EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network
title_short	EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network
title_full	EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network
title_fullStr	EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network
title_full_unstemmed	EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network
title_sort	eoesgc: predicting mirna-disease associations based on embedding of embedding and simplified graph convolutional network
publisher	BMC
publishDate	2021
url	https://doaj.org/article/7478900349554ad7bdfbdd107802fd92
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EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

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