miTarDigger: A Fusion Deep-learning Approach for Predicting Human miRNA Targets

MicroRNAs (miRNAs) are small non-coding RNAs that achieve post-transcriptional regulation of RNA silencing and gene expression by targeting messenger RNAs (mRNAs). Rapid and effective detection of miRNAs target sites is a significantly important topic in bioinformatics. In this study, a deep learnin...

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Vydáno v:2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) s. 2891 - 2897
Hlavní autoři: Yan, Jianrong, Li, Yanan, Zhu, Min
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 16.12.2020
Témata:
convolutional denoising autoencoders
Decoding
deep learning
Feature extraction
Immune system
miRNA
miRNA target site
Noise reduction
Predictive models
Regulation
RNA
stacked denoising autoencoders
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Abstract MicroRNAs (miRNAs) are small non-coding RNAs that achieve post-transcriptional regulation of RNA silencing and gene expression by targeting messenger RNAs (mRNAs). Rapid and effective detection of miRNAs target sites is a significantly important topic in bioinformatics. In this study, a deep learning approach based on fusion of stacked denoising autoencoders (SDA) and Convolutional denoising autoencoders (CAE) is developed for sequence and structure data respectively with the help of an existing duplex sequence model. Compared with four conventional machine learning methods, the proposed fusion model performs better in terms of the accuracy, precision, recall, AUC (Area under the curve) and Fl-score. A web system is also developed to identify and display the microRNA target sites effectively and Rapidly.
AbstractList MicroRNAs (miRNAs) are small non-coding RNAs that achieve post-transcriptional regulation of RNA silencing and gene expression by targeting messenger RNAs (mRNAs). Rapid and effective detection of miRNAs target sites is a significantly important topic in bioinformatics. In this study, a deep learning approach based on fusion of stacked denoising autoencoders (SDA) and Convolutional denoising autoencoders (CAE) is developed for sequence and structure data respectively with the help of an existing duplex sequence model. Compared with four conventional machine learning methods, the proposed fusion model performs better in terms of the accuracy, precision, recall, AUC (Area under the curve) and Fl-score. A web system is also developed to identify and display the microRNA target sites effectively and Rapidly.
Author Yan, Jianrong
Zhu, Min
Li, Yanan
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  givenname: Min
  surname: Zhu
  fullname: Zhu, Min
  email: zhumin@scu.edu.cn
  organization: Sichuan University,College of Computer Science,Chengdu,Country,610065
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Snippet MicroRNAs (miRNAs) are small non-coding RNAs that achieve post-transcriptional regulation of RNA silencing and gene expression by targeting messenger RNAs...
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StartPage 2891
SubjectTerms convolutional denoising autoencoders
Decoding
deep learning
Feature extraction
Immune system
miRNA
miRNA target site
Noise reduction
Predictive models
Regulation
RNA
stacked denoising autoencoders
Title miTarDigger: A Fusion Deep-learning Approach for Predicting Human miRNA Targets
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