ArticleBioinformatics (Oxford, England)2018
Convolutional neural networks for classification of alignments of non-coding RNA sequences.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- CMV-GLA: contrastive multi-view graph layer attention for predicting phosphorylation site-disease associations.BMC bioinformatics · 2026Article
- From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling.Briefings in bioinformatics · 2025Review
- Predict SARS-CoV-2 genome interactions based on RNA language models.Biosafety and health · 2025Article
- Review
- sRNAdeep: a novel tool for bacterial sRNA prediction based on DistilBERT encoding mode and deep learning algorithms.BMC genomics · 2024Article
- Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network.Interdisciplinary sciences, computational life sciences · 2024Article
- Big data and deep learning for RNA biology.Experimental & molecular medicine · 2024Review
- ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences.Microbiome · 2024Article
- A novel deep generative model for mRNA vaccine development: Designing 5' UTRs withActa pharmaceutica Sinica. B · 2024Article
- Predicting circRNA-miRNA interactions utilizing transformer-based RNA sequential learning and high-order proximity preserved embedding.iScience · 2024Article
- Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence.Marine drugs · 2023Review
- Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.PLoS computational biology · 2023Article
- Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE.BMC biology · 2023Article
- Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning.International journal of molecular sciences · 2022Review
- Model-guided engineering of DNA sequences with predictable site-specific recombination rates.Nature communications · 2022Article
- Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning.NAR genomics and bioinformatics · 2022Article
- Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs.International journal of molecular sciences · 2021Review
- Deep forest ensemble learning for classification of alignments of non-coding RNA sequences based on multi-view structure representations.Briefings in bioinformatics · 2021Article
- A deep learning framework combined with word embedding to identify DNA replication origins.Scientific reports · 2021Article
- Expression of Human Endogenous Retroviruses in Systemic Lupus Erythematosus: Multiomic Integration With Gene Expression.Frontiers in immunology · 2021Article
Corrections and comments
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2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: The convolutional neural network (CNN) has been applied to the classification problem of DNA sequences, with the additional purpose of motif discovery. The training of CNNs with distributed representations of four nucleotides has successfully derived position weight matrices on the learned kernels that corresponded to sequence motifs such as protein-binding sites. Results: We propose a novel application of CNNs to classification of pairwise alignments of sequences for accurate clustering of sequences and show the benefits of the CNN method of inputting pairwise alignments for clustering of non-coding RNA (ncRNA) sequences and for motif discovery. Classification of a pairwise alignment of two sequences into positive and negative classes corresponds to the clustering of the input sequences. After we combined the distributed representation of RNA nucleotides with the secondary-structure information specific to ncRNAs and furthermore with mapping profiles of next-generation sequence reads, the training of CNNs for classification of alignments of RNA sequences yielded accurate clustering in terms of ncRNA families and outperformed the existing clustering methods for ncRNA sequences. Several interesting sequence motifs and secondary-structure motifs known for the snoRNA family and specific to microRNA and tRNA families were identified. Availability and implementation: The source code of our CNN software in the deep-learning framework Chainer is available at http://www.dna.bio.keio.ac.jp/cnn/, and the dataset used for performance evaluation in this work is available at the same URL.
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