ArticleNature communications2020
Sequence-to-function deep learning frameworks for engineered riboregulators.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 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
59 citing papers in PubMed.
- Review
- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
- Crowdsourced riboregulators reveal design principles for programmable RNA switching.bioRxiv : the preprint server for biology · 2026Article
- RATEX: A Scalable RNA-Based Platform for Logical and Multi-Layered Cellular Programming.Angewandte Chemie (International ed. in English) · 2026Article
- Machine Learning for CRISPR-Based Diagnostics.International journal of molecular sciences · 2026Review
- Attenuated Bacteria-Based Tumor Therapy: Clinical Application Risks, Marketing Approval Restrictions, and Coping Strategies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Iterative design of a NAND hybrid riboswitch by deep batch Bayesian optimization.Nucleic acids research · 2026Article
- Toehold-VISTA: a machine learning approach to decipher programmable RNA sensor-target interactions.Nucleic acids research · 2026Article
- SUPER: Upcycling Genetic Parts for Precise Gene Expression Control, Leakage Minimization, and Genetic Circuit Stability.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- Probiotic-Based Materials as Living Therapeutics.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- The renaissance ofFrontiers in immunology · 2026Review
- Design of orthogonal and portable RNA devices for post-transcriptional regulation by reverse engineering type I toxin-antitoxin systems.Nucleic acids research · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- A generalized and efficient approach for complete mRNA design improves translation, stability and specificity.bioRxiv : the preprint server for biology · 2025Article
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- Generative and predictive neural networks for the design of functional RNA molecules.Nature communications · 2025Article
- Programmable trans-splicing riboregulators for complex cellular logic computation.Nature chemical biology · 2025Article
- Machine Learning for RNA Design: LEARNA.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Construction of multilayered gene circuits using de-novo-designed synthetic transcriptional regulators in cell-free systems.Journal of biological engineering · 2024Article
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
No grant is acknowledged in the PubMed record.
Abstract
While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules. Toehold switches, which are programmable nucleic acid sensors, face an analogous design bottleneck; our limited understanding of how sequence impacts functionality often necessitates expensive, time-consuming screens to identify effective switches. Here, we introduce Sequence-based Toehold Optimization and Redesign Model (STORM) and Nucleic-Acid Speech (NuSpeak), two orthogonal and synergistic deep learning architectures to characterize and optimize toeholds. Applying techniques from computer vision and natural language processing, we 'un-box' our models using convolutional filters, attention maps, and in silico mutagenesis. Through transfer-learning, we redesign sub-optimal toehold sensors, even with sparse training data, experimentally validating their improved performance. This work provides sequence-to-function deep learning frameworks for toehold selection and design, augmenting our ability to construct potent biological circuit components and precision diagnostics.
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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.