ArticleeLife2026
Generative modeling for RNA splicing prediction and design.
Article in eLife, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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Who cites it
10 citing papers in PubMed.
- Interpretable distillation reveals that deep learning splicing models suffer from pervasive confounders and blind spots.Genome biology · 2026Article
- Predicting human mRNA isoform levels from site-specific splicing kineticsbioRxiv : the preprint server for biology · 2026Article
- Improving splice site usage prediction with SPLAIRE.bioRxiv : the preprint server for biology · 2026Article
- DeepSAP: improved RNA-seq alignment by integrating transcriptome guidance with transformer-based splice junction scoring.Genome biology · 2026Article
- Detection of alternative splicing: deep sequencing or deep learning?Briefings in bioinformatics · 2026Article
- Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy.Frontiers in cell and developmental biology · 2026Review
- A deep dive into statistical modeling of RNA splicing QTLs reveals variants that explain neurodegenerative disease.American journal of human genetics · 2025Article
- Generative Design of Cell Type-Specific RNA Splicing Elements for Programmable Gene Regulation.bioRxiv : the preprint server for biology · 2025Article
- Toward a comprehensive profiling of alternative splicing proteoform structures, interactions and functions.Current opinion in structural biology · 2025Review
- Machine learning-optimized targeted detection of alternative splicing.Nucleic acids research · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Alternative splicing (AS) of pre-mRNA plays a crucial role in tissue-specific gene regulation, with disease implications due to splicing defects. Predicting and manipulating AS can therefore uncover new regulatory mechanisms and aid in therapeutic design. We introduce TrASPr+BOS, a generative AI model with Bayesian Optimization for predicting and designing RNA for tissue-specific splicing outcomes. Transformer for Alternative Splicing Prediction (TrASPr) is a multi-transformer model that can handle different types of AS events and generalize to unseen cellular conditions. It then serves as an oracle, generating labeled data to train a Bayesian Optimization for Splicing (BOS) algorithm to design RNA for condition-specific splicing outcomes. We show TrASPr+BOS outperforms existing methods, enhancing tissue-specific AUPRC by up to 1.8-fold and capturing tissue-specific regulatory elements. We validate hundreds of predicted novel tissue-specific splicing variations and confirm new regulatory elements using dCas13. We envision TrASPr+BOS as a light yet accurate method researchers can probe or adopt for specific tasks.
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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.