ArticleScientific reports2019
Nucleotide-level Convolutional Neural Networks for Pre-miRNA Classification.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- mirtronDB 2.0: enhanced database with novel mirtron discoveries.Bioinformatics (Oxford, England) · 2026Article
- GENNUS: generative approaches for nucleotide sequences enhance mirtron classification.NAR genomics and bioinformatics · 2025Article
- Description Generation Using Variational Auto-Encoders for Precursor microRNA.Entropy (Basel, Switzerland) · 2024Article
- GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides.Scientific reports · 2024Article
- A hybrid CNN-LSTM model for pre-miRNA classification.Scientific reports · 2021Article
- Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA.Frontiers in public health · 2021Article
- Article
- Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction.Scientific reports · 2020Article
- A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods.Current genomics · 2020Review
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4 authors.
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Abstract
Due to the biogenesis difference, miRNAs can be divided into canonical microRNAs and mirtrons. Compared to canonical microRNAs, mirtrons are less conserved and hard to be identified. Except stringent annotations based on experiments, many in silico computational methods have be developed to classify miRNAs. Although several machine learning classifiers delivered high classification performance, all the predictors depended heavily on the selection of calculated features. Here, we introduced nucleotide-level convolutional neural networks (CNNs) for pre-miRNAs classification. By using "one-hot" encoding and padding, pre-miRNAs were converted into matrixes with the same shape. The convolution and max-pooling operations can automatically extract features from pre-miRNAs sequences. Evaluation on test dataset showed that our models had a satisfactory performance. Our investigation showed that it was feasible to apply CNNs to extract features from biological sequences. Since there are many hyperparameters can be tuned in CNNs, we believe that the performance of nucleotide-level convolutional neural networks can be greatly improved in the future.
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