ArticlePLoS computational biology2024
Comparison and benchmark of deep learning methods for non-coding RNA classification.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Tree_RNA-Align: RNA Secondary Structure Clustering and Classification Based on Tree-Structure Alignment.International journal of molecular sciences · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 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
- MMnc: multi-modal interpretable representation for non-coding RNA classification and class annotation.Bioinformatics (Oxford, England) · 2025Article
- Improving ncRNA family prediction using multi-modal contrastive learning of sequence and structure.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
The involvement of non-coding RNAs in biological processes and diseases has made the exploration of their functions crucial. Most non-coding RNAs have yet to be studied, creating the need for methods that can rapidly classify large sets of non-coding RNAs into functional groups, or classes. In recent years, the success of deep learning in various domains led to its application to non-coding RNA classification. Multiple novel architectures have been developed, but these advancements are not covered by current literature reviews. We present an exhaustive comparison of the different methods proposed in the state-of-the-art and describe their associated datasets. Moreover, the literature lacks objective benchmarks. We perform experiments to fairly evaluate the performance of various tools for non-coding RNA classification on popular datasets. The robustness of methods to non-functional sequences and sequence boundary noise is explored. We also measure computation time and CO2 emissions. With regard to these results, we assess the relevance of the different architectural choices and provide recommendations to consider in future methods.
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Registered trials
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