ReviewComputational and structural biotechnology journal2025
Transformers in RNA structure prediction: A review.
Review in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- Targeting Non-coding RNAs in Neurodegeneration: Advances in Therapeutic RNA Modalities and Next-Gen Delivery Technologies.Current Alzheimer research · 2026Review
- ProNA3D: Distance-Based Analysis of Nucleic Acid-Containing Interfaces.Computational and structural biotechnology journal · 2026Article
- Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Transformer is a deep neural network based on the self-attention mechanism, designed to handle sequential data. Given its tremendous advantages in natural language processing, it has gained traction for other applications. As the primary structure of RNA is a sequence of nucleotides, researchers have applied Transformers to predict secondary and tertiary structures from RNA sequences. The number of Transformer-based models in structure prediction tasks is rapidly increasing as they have performed on par or better than other deep learning networks, such as Convolutional and Recurrent Neural Networks. This article thoroughly examines Transformer-based RNA structure prediction models. Through an in-depth analysis of the models, we aim to explain how their architectural innovations improve their performances and what they still lack. As Transformer-based techniques for RNA structure prediction continue to evolve, this review serves as both a record of past achievements and a guide for future avenues.
Indexed as
Identifiers
What OpenQuestion holds
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.