Abstract
MicroRNAs (miRNAs) are small noncoding RNAs that derived from hairpin-forming miRNA precursors (pre-miRNAs) and regulating gene expression at the post-transcriptional level. Many sophisticated computational tools have been developed for miRNA prediction. However, all these existing approaches for predicting miRNA require large amounts of task-specific knowledge in the form of handcrafted features and data pre-processing. In this article, we introduce MiRNN (MiRNN is available at https://github.com/CadenC/MiRNN), a novel computational predictor based on bidirectional gated recurrent units (GRUs). Our system is truly end-to-end, requiring no feature engineering or data preprocessing, thus making it applicable to a wide range of sequence classification tasks. Its main purpose is to omit the procedure of feature extraction and to provide accurate prediction by using the high-level features extracted from the bidirectional recurrent neural network. The experimental results show that MiRNN can produce state-of-the-art performance on pre-miRNA prediction task. The overall prediction accuracy of our model on miRBase data sets is 93.70%. In addition, we trained our model on various clade specific dataset and obtained increased accuracy.
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Cao, M., Li, D., Lin, Z., Niu, C., Ding, C. (2018). MiRNN: An Improved Prediction Model of MicroRNA Precursors Using Gated Recurrent Units. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_26
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DOI: https://doi.org/10.1007/978-3-319-95933-7_26
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