ArticleCommunications biology2026
Retentive Network promotes efficient RNA language modeling of long sequences.
Article in Communications biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Retentive Network promotes efficient RNA language modeling of long sequences.Communications biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The latent features of RNA sequences are crucial for our understanding of their functions. Thus, Transformer-based nucleotide language models have received widespread attention; however, the O(n²) complexity of Transformer limits their ability to process long sequences. In this work, we propose RNAret, an RNA language model based on Retention Network, which achieves training parallelism, low computational overhead, and long-sequence processing through a retention mechanism, with O(n) complexity. We pretrain RNAret using a self-supervised masked language modeling approach on 29.8 million RNA sequences. Experiments demonstrate the merit of RNAret as an RNA language model, achieving superior performance on a range of tasks, including RNA-RNA interaction prediction, RNA secondary structure prediction, and mRNA/lncRNA classification. RNAret shows strong potential for extracting latent features from RNA sequences and advancing our understanding of RNA biology.
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.