ArticleNAR genomics and bioinformatics2022
Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning.
Article in NAR genomics and bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
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Who cites it
77 citing papers in PubMed.
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- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- RNARL: reinforcement learning-driven unified generative framework for multi-objective RNA codon design.Genome biology · 2026Article
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- Foundation models in omics research: a comprehensive survey.Briefings in bioinformatics · 2026Review
- An overview of self-supervised deep learning applications to molecular data.Briefings in bioinformatics · 2026Review
- Sequence determinant and functional relevance of 8-oxoguanine RNA modification unveiled from foundation-model-based predictor.Molecular therapy. Nucleic acids · 2026Article
- Benchmarking reveals the superiority of nucleic acid foundation models in predicting lncRNA coding potential.Genome biology · 2026Article
- CodonMoE: DNA language models for codon-dependent mRNA prediction.Bioinformatics (Oxford, England) · 2026Article
- Detecting and quantifying overparametrization in RNA language models with REDIAL.bioRxiv : the preprint server for biology · 2026Article
- Graph-based RNA structural representation reveals determinants of subcellular localization.Briefings in bioinformatics · 2026Article
- Factorization machine with quadratic-optimization annealing for RNA inverse folding and evaluation of binary-integer encoding and nucleotide assignment.Scientific reports · 2026Article
- Decoding the language of messenger RNA.Nature methods · 2026Article
- UTR-DynaPro: a CNN-transformer multimodal language model for decoding 5'UTR regulatory mechanisms.Scientific reports · 2026Article
- What does it take to learn the rules of RNA base pairing? A lot less than you may think.Communications biology · 2026Article
- Retentive Network promotes efficient RNA language modeling of long sequences.Communications biology · 2026Article
17 more citing papers are in PubMed but not listed here.
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Effective embedding is actively conducted by applying deep learning to biomolecular information. Obtaining better embeddings enhances the quality of downstream analyses, such as DNA sequence motif detection and protein function prediction. In this study, we adopt a pre-training algorithm for the effective embedding of RNA bases to acquire semantically rich representations and apply this algorithm to two fundamental RNA sequence problems: structural alignment and clustering. By using the pre-training algorithm to embed the four bases of RNA in a position-dependent manner using a large number of RNA sequences from various RNA families, a context-sensitive embedding representation is obtained. As a result, not only base information but also secondary structure and context information of RNA sequences are embedded for each base. We call this 'informative base embedding' and use it to achieve accuracies superior to those of existing state-of-the-art methods on RNA structural alignment and RNA family clustering tasks. Furthermore, upon performing RNA sequence alignment by combining this informative base embedding with a simple Needleman-Wunsch alignment algorithm, we succeed in calculating structural alignments with a time complexity of
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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.