ArticleNature communications2020
A deep learning approach to programmable RNA switches.
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 76 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.
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
76 citing papers in PubMed, 142 citations in OpenAlex.
- Mammalian therapeutic riboswitches: Engineering and custom ligands.Molecular therapy. Nucleic acids · 2026Review
- 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
- Convergence of Soft Electronics and Artificial Intelligence: From Materials to Intelligent Systems.Nano-micro letters · 2026Review
- RATEX: A Scalable RNA-Based Platform for Logical and Multi-Layered Cellular Programming.Angewandte Chemie (International ed. in English) · 2026Article
- Iterative design of a NAND hybrid riboswitch by deep batch Bayesian optimization.Nucleic acids research · 2026Article
- RNA-Based Communication in Heterogeneous Populations of Cell Mimics.ACS synthetic biology · 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
- Base gap switches as transcriptional regulators and their applications as biosensors.Nature sensors · 2026Article
- Designing molecular RNA switches with Restricted Boltzmann machines.Nature communications · 2025Article
- Design of orthogonal and portable RNA devices for post-transcriptional regulation by reverse engineering type I toxin-antitoxin systems.Nucleic acids research · 2025Article
- Data-driven synthetic microbes for sustainable future.NPJ systems biology and applications · 2025Review
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Design of function-regulating RNA via deep learning and AlphaFold 3.Briefings in bioinformatics · 2025Article
- 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
- Improving the generalization of protein expression models with mechanistic sequence information.Nucleic acids research · 2025Article
16 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesize and characterize in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperform (R
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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.