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SiRNA silencing efficacy prediction based on a deep architecture

BACKGROUND: Small interfering RNA (siRNA) can be used to post-transcriptional gene regulation by knocking down targeted genes. In functional genomics, biomedical research and cancer therapeutics, siRNA design is a critical research topic. Various computational algorithms have been developed to selec...

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Detalles Bibliográficos
Autores principales: Han, Ye, He, Fei, Chen, Yongbing, Liu, Yuanning, Yu, Helong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157246/
https://www.ncbi.nlm.nih.gov/pubmed/30255786
http://dx.doi.org/10.1186/s12864-018-5028-8
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author Han, Ye
He, Fei
Chen, Yongbing
Liu, Yuanning
Yu, Helong
author_facet Han, Ye
He, Fei
Chen, Yongbing
Liu, Yuanning
Yu, Helong
author_sort Han, Ye
collection PubMed
description BACKGROUND: Small interfering RNA (siRNA) can be used to post-transcriptional gene regulation by knocking down targeted genes. In functional genomics, biomedical research and cancer therapeutics, siRNA design is a critical research topic. Various computational algorithms have been developed to select the most effective siRNA, whereas the efficacy prediction accuracy is not so satisfactory. Many existing computational methods are based on feature engineering, which may lead to biased and incomplete features. Deep learning utilizes non-linear mapping operations to detect potential feature pattern and has been considered perform better than existing machine learning method. RESULTS: In this paper, to further improve the prediction accuracy and facilitate gene functional studies, we developed a new powerful siRNA efficacy predictor based on a deep architecture. First, we extracted hidden feature patterns from two modalities, including sequence context features and thermodynamic property. Then, we constructed a deep architecture to implement the prediction. On the available largest siRNA database, the performance of our proposed method was measured with 0.725 PCC and 0.903 AUC value. The comparative experiment showed that our proposed architecture outperformed several siRNA prediction methods. CONCLUSIONS: The results demonstrate that our deep architecture is stable and efficient to predict siRNA silencing efficacy. The method could help select candidate siRNA for targeted mRNA, and further promote the development of RNA interference.
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spelling pubmed-61572462018-10-01 SiRNA silencing efficacy prediction based on a deep architecture Han, Ye He, Fei Chen, Yongbing Liu, Yuanning Yu, Helong BMC Genomics Research BACKGROUND: Small interfering RNA (siRNA) can be used to post-transcriptional gene regulation by knocking down targeted genes. In functional genomics, biomedical research and cancer therapeutics, siRNA design is a critical research topic. Various computational algorithms have been developed to select the most effective siRNA, whereas the efficacy prediction accuracy is not so satisfactory. Many existing computational methods are based on feature engineering, which may lead to biased and incomplete features. Deep learning utilizes non-linear mapping operations to detect potential feature pattern and has been considered perform better than existing machine learning method. RESULTS: In this paper, to further improve the prediction accuracy and facilitate gene functional studies, we developed a new powerful siRNA efficacy predictor based on a deep architecture. First, we extracted hidden feature patterns from two modalities, including sequence context features and thermodynamic property. Then, we constructed a deep architecture to implement the prediction. On the available largest siRNA database, the performance of our proposed method was measured with 0.725 PCC and 0.903 AUC value. The comparative experiment showed that our proposed architecture outperformed several siRNA prediction methods. CONCLUSIONS: The results demonstrate that our deep architecture is stable and efficient to predict siRNA silencing efficacy. The method could help select candidate siRNA for targeted mRNA, and further promote the development of RNA interference. BioMed Central 2018-09-24 /pmc/articles/PMC6157246/ /pubmed/30255786 http://dx.doi.org/10.1186/s12864-018-5028-8 Text en © The Author(s). 2018 Open AccessThis 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
Han, Ye
He, Fei
Chen, Yongbing
Liu, Yuanning
Yu, Helong
SiRNA silencing efficacy prediction based on a deep architecture
title SiRNA silencing efficacy prediction based on a deep architecture
title_full SiRNA silencing efficacy prediction based on a deep architecture
title_fullStr SiRNA silencing efficacy prediction based on a deep architecture
title_full_unstemmed SiRNA silencing efficacy prediction based on a deep architecture
title_short SiRNA silencing efficacy prediction based on a deep architecture
title_sort sirna silencing efficacy prediction based on a deep architecture
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157246/
https://www.ncbi.nlm.nih.gov/pubmed/30255786
http://dx.doi.org/10.1186/s12864-018-5028-8
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