ArticleNucleic acids research2025
Integrating experimental feedback improves generative models for biological sequences.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Generative artificial intelligence in animal genomics for smart agriculture: Applications, challenges, and future prospects.Veterinary and animal science · 2026Review
- From Prediction to Creation: Generative Plant Design.Plants (Basel, Switzerland) · 2026Review
- BlendSplice: A Frequency-Blended Generative Framework forComputational and structural biotechnology journal · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Generative probabilistic models have shown promise in designing artificial RNA and protein sequences but often suffer from high rates of false positives, where sequences predicted as functional fail experimental validation. To address this critical limitation, we explore the impact of reintegrating experimental feedback into the model design process. We propose a likelihood-based reintegration scheme, which we test through extensive computational experiments on both RNA and protein datasets, as well as through wet-lab experiments on the self-splicing ribozyme from the Group I intron RNA family where our approach demonstrates particular efficacy. We show that integrating recent experimental data enhances the model's capacity of generating functional sequences (e.g. from 6.7% to 63.7% of active designs at 45 mutations). This feedback-driven approach thus provides a significant improvement in the design of biomolecular sequences by directly tackling the false-positive challenge.
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Registered trials
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