ArticleBioData mining2017
nRC: non-coding RNA Classifier based on structural features.
Article in BioData mining, 2017. 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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30 citing papers in PubMed, 99 citations in OpenAlex.
- A comprehensive survey of genome language models in bioinformatics.Briefings in bioinformatics · 2026Review
- Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications.Nature communications · 2025Article
- Noncoding RNA family classification based on multifeature fusion and convolutional block attention residual network.Briefings in bioinformatics · 2025Article
- Interpretation of RNA Universe and Coding Potential Using IntRNA.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- MMnc: multi-modal interpretable representation for non-coding RNA classification and class annotation.Bioinformatics (Oxford, England) · 2025Article
- Article
- CGSDA: inferring snoRNA-disease associations via ChebNetII and GatedGCN.Frontiers in genetics · 2025Article
- GL4SDA: Predicting snoRNA-disease associations using GNNs and LLM embeddings.Computational and structural biotechnology journal · 2025Article
- Improving ncRNA family prediction using multi-modal contrastive learning of sequence and structure.Bioinformatics (Oxford, England) · 2024Article
- Comparison and benchmark of deep learning methods for non-coding RNA classification.PLoS computational biology · 2024Article
- BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification.RNA biology · 2024Article
- GraphPart: homology partitioning for biological sequence analysis.NAR genomics and bioinformatics · 2023Article
- ConF: A Deep Learning Model Based on BiLSTM, CNN, and Cross Multi-Head Attention Mechanism for Noncoding RNA Family Prediction.Biomolecules · 2023Article
- High-Accuracy ncRNA Function Prediction via Deep Learning Using Global and Local Sequence Information.Biomedicines · 2023Article
- MncR: Late Integration Machine Learning Model for Classification of ncRNA Classes Using Sequence and Structural Encoding.International journal of molecular sciences · 2023Article
- The Potential Biological Roles of Circular RNAs in the Immune Systems of Insects to Pathogen Invasion.Genes · 2023Review
- A TRIzol-based method for high recovery of plasma sncRNAs approximately 30 to 60 nucleotides.Scientific reports · 2022Article
- Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants.Frontiers in plant science · 2022Article
- MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique.Frontiers in medicine · 2022Article
- NCodR: A multi-class support vector machine classification to distinguish non-coding RNAs in Viridiplantae.Quantitative plant biology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationNon-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs. In this work, we introduce a new ncRNA classification tool, nRC (non-coding RNA Classifier). Our approach is based on features extraction from the ncRNA secondary structure together with a supervised classification algorithm implementing a deep learning architecture based on convolutional neural networks.
resultsWe tested our approach for the classification of 13 different ncRNA classes. We obtained classification scores, using the most common statistical measures. In particular, we reach an accuracy and sensitivity score of about 74%.
conclusionThe proposed method outperforms other similar classification methods based on secondary structure features and machine learning algorithms, including the RNAcon tool that, to date, is the reference classifier. nRC tool is freely available as a docker image at https://hub.docker.com/r/tblab/nrc/. The source code of nRC tool is also available at https://github.com/IcarPA-TBlab/nrc.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.