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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-6321160 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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