ArticlePLoS computational biology2020
Deep learning predicts short non-coding RNA functions from only raw sequence data.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 41 citations in OpenAlex.
- ERNIE-RNA: an RNA language model with structure-enhanced representations.Nature communications · 2025Article
- Interpretation of RNA Universe and Coding Potential Using IntRNA.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Noncoding RNA family classification based on multifeature fusion and convolutional block attention residual network.Briefings in bioinformatics · 2025Article
- MMnc: multi-modal interpretable representation for non-coding RNA classification and class annotation.Bioinformatics (Oxford, England) · 2025Article
- Evaluation of the role of circular RNA (circ-FAF1) as a diagnostic biomarker for breast cancer in a cohort of Egyptian breast cancer patients.Molecular biology reports · 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
- Molecular insights into regulatory RNAs in the cellular machinery.Experimental & molecular medicine · 2024Review
- MFPINC: prediction of plant ncRNAs based on multi-source feature fusion.BMC genomics · 2024Article
- BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification.RNA biology · 2024Article
- 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
- ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction.BMC bioinformatics · 2023Article
- PINC: A Tool for Non-Coding RNA Identification in Plants Based on an Automated Machine Learning Framework.International journal of molecular sciences · 2022Article
- Non-coding RNAs in malaria infection.Wiley interdisciplinary reviews. RNA · 2022Review
- The role of noncoding RNAs in cancer lipid metabolism.Frontiers in oncology · 2022Review
- Advances in Non-Coding RNA Sequencing.Non-coding RNA · 2021Review
- Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has demanded new computational approaches to annotate their roles. It is widely known that secondary structure is determinant to know RNA function and machine learning based approaches have been successfully proven to predict RNA function from secondary structure information. Here we show that RNA function can be predicted with good accuracy from a lightweight representation of sequence information without the necessity of computing secondary structure features which is computationally expensive. This finding appears to go against the dogma of secondary structure being a key determinant of function in RNA. Compared to recent secondary structure based methods, the proposed solution is more robust to sequence boundary noise and reduces drastically the computational cost allowing for large data volume annotations. Scripts and datasets to reproduce the results of experiments proposed in this study are available at: https://github.com/bioinformatics-sannio/ncrna-deep.
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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.