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Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs

Identification of disease-related microRNAs (disease miRNAs) is helpful for understanding and exploring the etiology and pathogenesis of diseases. Most of recent methods predict disease miRNAs by integrating the similarities and associations of miRNAs and diseases. However, these methods fail to lea...

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Detalles Bibliográficos
Autores principales: Xuan, Ping, Dong, Yihua, Guo, Yahong, Zhang, Tiangang, Liu, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6321160/
https://www.ncbi.nlm.nih.gov/pubmed/30477152
http://dx.doi.org/10.3390/ijms19123732
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author Xuan, Ping
Dong, Yihua
Guo, Yahong
Zhang, Tiangang
Liu, Yong
author_facet Xuan, Ping
Dong, Yihua
Guo, Yahong
Zhang, Tiangang
Liu, Yong
author_sort Xuan, Ping
collection PubMed
description Identification of disease-related microRNAs (disease miRNAs) is helpful for understanding and exploring the etiology and pathogenesis of diseases. Most of recent methods predict disease miRNAs by integrating the similarities and associations of miRNAs and diseases. However, these methods fail to learn the deep features of the miRNA similarities, the disease similarities, and the miRNA–disease associations. We propose a dual convolutional neural network-based method for predicting candidate disease miRNAs and refer to it as CNNDMP. CNNDMP not only exploits the similarities and associations of miRNAs and diseases, but also captures the topology structures of the miRNA and disease networks. An embedding layer is constructed by combining the biological premises about the miRNA–disease associations. A new framework based on the dual convolutional neural network is presented for extracting the deep feature representation of associations. The left part of the framework focuses on integrating the original similarities and associations of miRNAs and diseases. The novel miRNA and disease similarities which contain the topology structures are obtained by random walks on the miRNA and disease networks, and their deep features are learned by the right part of the framework. CNNDMP achieves the superior prediction performance than several state-of-the-art methods during the cross-validation process. Case studies on breast cancer, colorectal cancer and lung cancer further demonstrate CNNDMP’s powerful ability of discovering potential disease miRNAs.
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spelling pubmed-63211602019-01-07 Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs Xuan, Ping Dong, Yihua Guo, Yahong Zhang, Tiangang Liu, Yong Int J Mol Sci Article Identification of disease-related microRNAs (disease miRNAs) is helpful for understanding and exploring the etiology and pathogenesis of diseases. Most of recent methods predict disease miRNAs by integrating the similarities and associations of miRNAs and diseases. However, these methods fail to learn the deep features of the miRNA similarities, the disease similarities, and the miRNA–disease associations. We propose a dual convolutional neural network-based method for predicting candidate disease miRNAs and refer to it as CNNDMP. CNNDMP not only exploits the similarities and associations of miRNAs and diseases, but also captures the topology structures of the miRNA and disease networks. An embedding layer is constructed by combining the biological premises about the miRNA–disease associations. A new framework based on the dual convolutional neural network is presented for extracting the deep feature representation of associations. The left part of the framework focuses on integrating the original similarities and associations of miRNAs and diseases. The novel miRNA and disease similarities which contain the topology structures are obtained by random walks on the miRNA and disease networks, and their deep features are learned by the right part of the framework. CNNDMP achieves the superior prediction performance than several state-of-the-art methods during the cross-validation process. Case studies on breast cancer, colorectal cancer and lung cancer further demonstrate CNNDMP’s powerful ability of discovering potential disease miRNAs. MDPI 2018-11-23 /pmc/articles/PMC6321160/ /pubmed/30477152 http://dx.doi.org/10.3390/ijms19123732 Text en © 2018 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
Xuan, Ping
Dong, Yihua
Guo, Yahong
Zhang, Tiangang
Liu, Yong
Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs
title Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs
title_full Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs
title_fullStr Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs
title_full_unstemmed Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs
title_short Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs
title_sort dual convolutional neural network based method for predicting disease-related mirnas
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6321160/
https://www.ncbi.nlm.nih.gov/pubmed/30477152
http://dx.doi.org/10.3390/ijms19123732
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