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Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease
Many studies have suggested that lncRNAs are involved in distinct and diverse biological processes. The mutation of lncRNAs plays a major role in a wide range of diseases. A comprehensive information of lncRNA-disease associations would improve our understanding of the underlying molecular mechanism...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6795129/ https://www.ncbi.nlm.nih.gov/pubmed/31649723 http://dx.doi.org/10.3389/fgene.2019.00937 |
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author | Hu, Jialu Gao, Yiqun Li, Jing Shang, Xuequn |
author_facet | Hu, Jialu Gao, Yiqun Li, Jing Shang, Xuequn |
author_sort | Hu, Jialu |
collection | PubMed |
description | Many studies have suggested that lncRNAs are involved in distinct and diverse biological processes. The mutation of lncRNAs plays a major role in a wide range of diseases. A comprehensive information of lncRNA-disease associations would improve our understanding of the underlying molecular mechanism that can explain the development of disease. However, the discovery of the relationship between lncRNA and disease in biological experiment is costly and time-consuming. Although many computational algorithms have been proposed in the last decade, there still exists much room to improve because of diverse computational limitations. In this paper, we proposed a deep-learning framework, NNLDA, to predict potential lncRNA-disease associations. We compared it with other two widely-used algorithms on a network with 205,959 interactions between 19,166 lncRNAs and 529 diseases. Results show that NNLDA outperforms other existing algorithm in the prediction of lncRNA-disease association. Additionally, NNLDA can be easily applied to large-scale datasets using the technique of mini-batch stochastic gradient descent. To our best knowledge, NNLDA is the first algorithm that uses deep neural networks to predict lncRNA-disease association. The source code of NNLDA can be freely accessed at https://github.com/gao793583308/NNLDA. |
format | Online Article Text |
id | pubmed-6795129 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67951292019-10-24 Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease Hu, Jialu Gao, Yiqun Li, Jing Shang, Xuequn Front Genet Genetics Many studies have suggested that lncRNAs are involved in distinct and diverse biological processes. The mutation of lncRNAs plays a major role in a wide range of diseases. A comprehensive information of lncRNA-disease associations would improve our understanding of the underlying molecular mechanism that can explain the development of disease. However, the discovery of the relationship between lncRNA and disease in biological experiment is costly and time-consuming. Although many computational algorithms have been proposed in the last decade, there still exists much room to improve because of diverse computational limitations. In this paper, we proposed a deep-learning framework, NNLDA, to predict potential lncRNA-disease associations. We compared it with other two widely-used algorithms on a network with 205,959 interactions between 19,166 lncRNAs and 529 diseases. Results show that NNLDA outperforms other existing algorithm in the prediction of lncRNA-disease association. Additionally, NNLDA can be easily applied to large-scale datasets using the technique of mini-batch stochastic gradient descent. To our best knowledge, NNLDA is the first algorithm that uses deep neural networks to predict lncRNA-disease association. The source code of NNLDA can be freely accessed at https://github.com/gao793583308/NNLDA. Frontiers Media S.A. 2019-10-09 /pmc/articles/PMC6795129/ /pubmed/31649723 http://dx.doi.org/10.3389/fgene.2019.00937 Text en Copyright © 2019 Hu, Gao, Li and Shang http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Hu, Jialu Gao, Yiqun Li, Jing Shang, Xuequn Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease |
title | Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease |
title_full | Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease |
title_fullStr | Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease |
title_full_unstemmed | Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease |
title_short | Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease |
title_sort | deep learning enables accurate prediction of interplay between lncrna and disease |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6795129/ https://www.ncbi.nlm.nih.gov/pubmed/31649723 http://dx.doi.org/10.3389/fgene.2019.00937 |
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