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nRC: non-coding RNA Classifier based on structural features
MOTIVATION: Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate betw...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5540506/ https://www.ncbi.nlm.nih.gov/pubmed/28785313 http://dx.doi.org/10.1186/s13040-017-0148-2 |
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author | Fiannaca, Antonino La Rosa, Massimo La Paglia, Laura Rizzo, Riccardo Urso, Alfonso |
author_facet | Fiannaca, Antonino La Rosa, Massimo La Paglia, Laura Rizzo, Riccardo Urso, Alfonso |
author_sort | Fiannaca, Antonino |
collection | PubMed |
description | MOTIVATION: Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs. In this work, we introduce a new ncRNA classification tool, nRC (non-coding RNA Classifier). Our approach is based on features extraction from the ncRNA secondary structure together with a supervised classification algorithm implementing a deep learning architecture based on convolutional neural networks. RESULTS: We tested our approach for the classification of 13 different ncRNA classes. We obtained classification scores, using the most common statistical measures. In particular, we reach an accuracy and sensitivity score of about 74%. CONCLUSION: The proposed method outperforms other similar classification methods based on secondary structure features and machine learning algorithms, including the RNAcon tool that, to date, is the reference classifier. nRC tool is freely available as a docker image at https://hub.docker.com/r/tblab/nrc/. The source code of nRC tool is also available at https://github.com/IcarPA-TBlab/nrc. |
format | Online Article Text |
id | pubmed-5540506 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55405062017-08-07 nRC: non-coding RNA Classifier based on structural features Fiannaca, Antonino La Rosa, Massimo La Paglia, Laura Rizzo, Riccardo Urso, Alfonso BioData Min Research MOTIVATION: Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs. In this work, we introduce a new ncRNA classification tool, nRC (non-coding RNA Classifier). Our approach is based on features extraction from the ncRNA secondary structure together with a supervised classification algorithm implementing a deep learning architecture based on convolutional neural networks. RESULTS: We tested our approach for the classification of 13 different ncRNA classes. We obtained classification scores, using the most common statistical measures. In particular, we reach an accuracy and sensitivity score of about 74%. CONCLUSION: The proposed method outperforms other similar classification methods based on secondary structure features and machine learning algorithms, including the RNAcon tool that, to date, is the reference classifier. nRC tool is freely available as a docker image at https://hub.docker.com/r/tblab/nrc/. The source code of nRC tool is also available at https://github.com/IcarPA-TBlab/nrc. BioMed Central 2017-08-01 /pmc/articles/PMC5540506/ /pubmed/28785313 http://dx.doi.org/10.1186/s13040-017-0148-2 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Fiannaca, Antonino La Rosa, Massimo La Paglia, Laura Rizzo, Riccardo Urso, Alfonso nRC: non-coding RNA Classifier based on structural features |
title | nRC: non-coding RNA Classifier based on structural features |
title_full | nRC: non-coding RNA Classifier based on structural features |
title_fullStr | nRC: non-coding RNA Classifier based on structural features |
title_full_unstemmed | nRC: non-coding RNA Classifier based on structural features |
title_short | nRC: non-coding RNA Classifier based on structural features |
title_sort | nrc: non-coding rna classifier based on structural features |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5540506/ https://www.ncbi.nlm.nih.gov/pubmed/28785313 http://dx.doi.org/10.1186/s13040-017-0148-2 |
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