A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network

Abstract Previous studies indicated that miRNA plays an important role in human biological processes especially in the field of diseases. However, constrained by biotechnology, only a small part of the miRNA-disease associations has been verified by biological experiment. This impel that more and mo...

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Autores principales:	Hao-Yuan Li, Hai-Yan Chen, Lei Wang, Shen-Jian Song, Zhu-Hong You, Xin Yan, Jin-Qian Yu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5cf8b559eacc474582a34e762013b501
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spelling	oai:doaj.org-article:5cf8b559eacc474582a34e762013b5012021-12-02T17:41:27ZA structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network10.1038/s41598-021-91991-w2045-2322https://doaj.org/article/5cf8b559eacc474582a34e762013b5012021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91991-whttps://doaj.org/toc/2045-2322Abstract Previous studies indicated that miRNA plays an important role in human biological processes especially in the field of diseases. However, constrained by biotechnology, only a small part of the miRNA-disease associations has been verified by biological experiment. This impel that more and more researchers pay attention to develop efficient and high-precision computational methods for predicting the potential miRNA-disease associations. Based on the assumption that molecules are related to each other in human physiological processes, we developed a novel structural deep network embedding model (SDNE-MDA) for predicting miRNA-disease association using molecular associations network. Specifically, the SDNE-MDA model first integrating miRNA attribute information by Chao Game Representation (CGR) algorithm and disease attribute information by disease semantic similarity. Secondly, we extract feature by structural deep network embedding from the heterogeneous molecular associations network. Then, a comprehensive feature descriptor is constructed by combining attribute information and behavior information. Finally, Convolutional Neural Network (CNN) is adopted to train and classify these feature descriptors. In the five-fold cross validation experiment, SDNE-MDA achieved AUC of 0.9447 with the prediction accuracy of 87.38% on the HMDD v3.0 dataset. To further verify the performance of SDNE-MDA, we contrasted it with different feature extraction models and classifier models. Moreover, the case studies with three important human diseases, including Breast Neoplasms, Kidney Neoplasms, Lymphoma were implemented by the proposed model. As a result, 47, 46 and 46 out of top-50 predicted disease-related miRNAs have been confirmed by independent databases. These results anticipate that SDNE-MDA would be a reliable computational tool for predicting potential miRNA-disease associations.Hao-Yuan LiHai-Yan ChenLei WangShen-Jian SongZhu-Hong YouXin YanJin-Qian YuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hao-Yuan Li Hai-Yan Chen Lei Wang Shen-Jian Song Zhu-Hong You Xin Yan Jin-Qian Yu A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network
description	Abstract Previous studies indicated that miRNA plays an important role in human biological processes especially in the field of diseases. However, constrained by biotechnology, only a small part of the miRNA-disease associations has been verified by biological experiment. This impel that more and more researchers pay attention to develop efficient and high-precision computational methods for predicting the potential miRNA-disease associations. Based on the assumption that molecules are related to each other in human physiological processes, we developed a novel structural deep network embedding model (SDNE-MDA) for predicting miRNA-disease association using molecular associations network. Specifically, the SDNE-MDA model first integrating miRNA attribute information by Chao Game Representation (CGR) algorithm and disease attribute information by disease semantic similarity. Secondly, we extract feature by structural deep network embedding from the heterogeneous molecular associations network. Then, a comprehensive feature descriptor is constructed by combining attribute information and behavior information. Finally, Convolutional Neural Network (CNN) is adopted to train and classify these feature descriptors. In the five-fold cross validation experiment, SDNE-MDA achieved AUC of 0.9447 with the prediction accuracy of 87.38% on the HMDD v3.0 dataset. To further verify the performance of SDNE-MDA, we contrasted it with different feature extraction models and classifier models. Moreover, the case studies with three important human diseases, including Breast Neoplasms, Kidney Neoplasms, Lymphoma were implemented by the proposed model. As a result, 47, 46 and 46 out of top-50 predicted disease-related miRNAs have been confirmed by independent databases. These results anticipate that SDNE-MDA would be a reliable computational tool for predicting potential miRNA-disease associations.
format	article
author	Hao-Yuan Li Hai-Yan Chen Lei Wang Shen-Jian Song Zhu-Hong You Xin Yan Jin-Qian Yu
author_facet	Hao-Yuan Li Hai-Yan Chen Lei Wang Shen-Jian Song Zhu-Hong You Xin Yan Jin-Qian Yu
author_sort	Hao-Yuan Li
title	A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network
title_short	A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network
title_full	A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network
title_fullStr	A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network
title_full_unstemmed	A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network
title_sort	structural deep network embedding model for predicting associations between mirna and disease based on molecular association network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5cf8b559eacc474582a34e762013b501
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_version_	1718379706806435840

A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network

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