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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Veröffentlicht in:2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) S. 2891 - 2897
Hauptverfasser: Yan, Jianrong, Li, Yanan, Zhu, Min
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 16.12.2020
Schlagworte:
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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Zusammenfassung: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