ArticleBMC bioinformatics2017
RNA inverse folding using Monte Carlo tree search.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Factorization machine with quadratic-optimization annealing for RNA inverse folding and evaluation of binary-integer encoding and nucleotide assignment.Scientific reports · 2026Article
- SamplingDesign: RNA design via continuous optimization with coupled variables and Monte-Carlo sampling.Nature communications · 2026Article
- Computational approaches for RNA structure prediction and design.Cell reports. Physical science · 2026Article
- Comprehensive datasets for RNA design, machine learning, and beyond.Scientific reports · 2025Article
- Datasets for Benchmarking RNA Design Algorithms.Methods in molecular biology (Clifton, N.J.) · 2025Article
- R3Design: deep tertiary structure-based RNA sequence design and beyond.Briefings in bioinformatics · 2024Article
- Deep generative design of RNA aptamers using structural predictions.Nature computational science · 2024Article
- RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models.Bioinformatics (Oxford, England) · 2024Article
- Partial RNA design.Bioinformatics (Oxford, England) · 2024Article
- RNA design via structure-aware multifrontier ensemble optimization.Bioinformatics (Oxford, England) · 2023Article
- Fitness functions for RNA structure design.Nucleic acids research · 2023Article
- aRNAque: an evolutionary algorithm for inverse pseudoknotted RNA folding inspired by Lévy flights.BMC bioinformatics · 2022Article
- EternaBrain: Automated RNA design through move sets and strategies from an Internet-scale RNA videogame.PLoS computational biology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificially synthesized RNA molecules provide important ways for creating a variety of novel functional molecules. State-of-the-art RNA inverse folding algorithms can design simple and short RNA sequences of specific GC content, that fold into the target RNA structure. However, their performance is not satisfactory in complicated cases.
resultWe present a new inverse folding algorithm called MCTS-RNA, which uses Monte Carlo tree search (MCTS), a technique that has shown exceptional performance in Computer Go recently, to represent and discover the essential part of the sequence space. To obtain high accuracy, initial sequences generated by MCTS are further improved by a series of local updates. Our algorithm has an ability to control the GC content precisely and can deal with pseudoknot structures. Using common benchmark datasets for evaluation, MCTS-RNA showed a lot of promise as a standard method of RNA inverse folding.
conclusionMCTS-RNA is available at https://github.com/tsudalab/MCTS-RNA .
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.