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CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach
Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role in a variety of biological activities. The interac...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806072/ https://www.ncbi.nlm.nih.gov/pubmed/35051187 http://dx.doi.org/10.1371/journal.pcbi.1009798 |
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author | Niu, Mengting Zou, Quan Lin, Chen |
author_facet | Niu, Mengting Zou, Quan Lin, Chen |
author_sort | Niu, Mengting |
collection | PubMed |
description | Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role in a variety of biological activities. The interactions between circRNAs and RBPs are key to comprehending the mechanism of posttranscriptional regulation. Accurately identifying binding sites is very useful for analyzing interactions. In past research, some predictors on the basis of machine learning (ML) have been presented, but prediction accuracy still needs to be ameliorated. Therefore, we present a novel calculation model, CRBPDL, which uses an Adaboost integrated deep hierarchical network to identify the binding sites of circular RNA-RBP. CRBPDL combines five different feature encoding schemes to encode the original RNA sequence, uses deep multiscale residual networks (MSRN) and bidirectional gating recurrent units (BiGRUs) to effectively learn high-level feature representations, it is sufficient to extract local and global context information at the same time. Additionally, a self-attention mechanism is employed to train the robustness of the CRBPDL. Ultimately, the Adaboost algorithm is applied to integrate deep learning (DL) model to improve prediction performance and reliability of the model. To verify the usefulness of CRBPDL, we compared the efficiency with state-of-the-art methods on 37 circular RNA data sets and 31 linear RNA data sets. Moreover, results display that CRBPDL is capable of performing universal, reliable, and robust. The code and data sets are obtainable at https://github.com/nmt315320/CRBPDL.git. |
format | Online Article Text |
id | pubmed-8806072 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-88060722022-02-02 CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach Niu, Mengting Zou, Quan Lin, Chen PLoS Comput Biol Research Article Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role in a variety of biological activities. The interactions between circRNAs and RBPs are key to comprehending the mechanism of posttranscriptional regulation. Accurately identifying binding sites is very useful for analyzing interactions. In past research, some predictors on the basis of machine learning (ML) have been presented, but prediction accuracy still needs to be ameliorated. Therefore, we present a novel calculation model, CRBPDL, which uses an Adaboost integrated deep hierarchical network to identify the binding sites of circular RNA-RBP. CRBPDL combines five different feature encoding schemes to encode the original RNA sequence, uses deep multiscale residual networks (MSRN) and bidirectional gating recurrent units (BiGRUs) to effectively learn high-level feature representations, it is sufficient to extract local and global context information at the same time. Additionally, a self-attention mechanism is employed to train the robustness of the CRBPDL. Ultimately, the Adaboost algorithm is applied to integrate deep learning (DL) model to improve prediction performance and reliability of the model. To verify the usefulness of CRBPDL, we compared the efficiency with state-of-the-art methods on 37 circular RNA data sets and 31 linear RNA data sets. Moreover, results display that CRBPDL is capable of performing universal, reliable, and robust. The code and data sets are obtainable at https://github.com/nmt315320/CRBPDL.git. Public Library of Science 2022-01-20 /pmc/articles/PMC8806072/ /pubmed/35051187 http://dx.doi.org/10.1371/journal.pcbi.1009798 Text en © 2022 Niu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Niu, Mengting Zou, Quan Lin, Chen CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach |
title | CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach |
title_full | CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach |
title_fullStr | CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach |
title_full_unstemmed | CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach |
title_short | CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach |
title_sort | crbpdl: identification of circrna-rbp interaction sites using an ensemble neural network approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806072/ https://www.ncbi.nlm.nih.gov/pubmed/35051187 http://dx.doi.org/10.1371/journal.pcbi.1009798 |
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