ArticlePLoS computational biology2019
EternaBrain: Automated RNA design through move sets and strategies from an Internet-scale RNA videogame.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- RNA design: update on computational frameworks and programs for inverse RNA folding.Briefings in bioinformatics · 2026Review
- Factorization machine with quadratic-optimization annealing for RNA inverse folding and evaluation of binary-integer encoding and nucleotide assignment.Scientific reports · 2026Article
- JAX-RNAfold: scalable differentiable folding.Bioinformatics (Oxford, England) · 2025Article
- New directions in mapping the Earth's surface with citizen science and generative AI.iScience · 2025Review
- DRAG: design RNAs as hierarchical graphs with reinforcement learning.Briefings in bioinformatics · 2025Article
- Machine Learning for RNA Design: LEARNA.Methods in molecular biology (Clifton, N.J.) · 2025Article
- EnrichRBP: an automated and interpretable computational platform for predicting and analysing RNA-binding protein events.Bioinformatics (Oxford, England) · 2024Article
- Authorship and Citizen Science: Seven Heuristic Rules.Science and engineering ethics · 2024Article
- RNAincoder: a deep learning-based encoder for RNA and RNA-associated interaction.Nucleic acids research · 2023Article
- Fitness functions for RNA structure design.Nucleic acids research · 2023Article
- Fast free-energy-based neutral set size estimates for the RNA genotype-phenotype map.Journal of the Royal Society, Interface · 2022Article
- RNA secondary structure prediction with convolutional neural networks.BMC bioinformatics · 2022Article
- Individual and collective human intelligence in drug design: evaluating the search strategy.Journal of cheminformatics · 2021Article
- RNA Engineering for Public Health: Innovations in RNA-Based Diagnostics and Therapeutics.Annual review of chemical and biomolecular engineering · 2021Article
- Machine Learning to Enhance Biophysical Models for Riboswitch Discovery.Wiley interdisciplinary reviews. RNAReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Emerging RNA-based approaches to disease detection and gene therapy require RNA sequences that fold into specific base-pairing patterns, but computational algorithms generally remain inadequate for these secondary structure design tasks. The Eterna project has crowdsourced RNA design to human video game players in the form of puzzles that reach extraordinary difficulty. Here, we demonstrate that Eterna participants' moves and strategies can be leveraged to improve automated computational RNA design. We present an eternamoves-large repository consisting of 1.8 million of player moves on 12 of the most-played Eterna puzzles as well as an eternamoves-select repository of 30,477 moves from the top 72 players on a select set of more advanced puzzles. On eternamoves-select, we present a multilayer convolutional neural network (CNN) EternaBrain that achieves test accuracies of 51% and 34% in base prediction and location prediction, respectively, suggesting that top players' moves are partially stereotyped. Pipelining this CNN's move predictions with single-action-playout (SAP) of six strategies compiled by human players solves 61 out of 100 independent puzzles in the Eterna100 benchmark. EternaBrain-SAP outperforms previously published RNA design algorithms and achieves similar or better performance than a newer generation of deep learning methods, while being largely orthogonal to these other methods. Our study provides useful lessons for future efforts to achieve human-competitive performance with automated RNA design algorithms.
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.