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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Popis
Shrnutí:	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.
DOI:	10.1109/BIBM49941.2020.9313504