ReviewMethods (San Diego, Calif.)2019
How to benchmark RNA secondary structure prediction accuracy.
Review in Methods (San Diego, Calif.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed.
- Markov models of SHAPE data improve secondary structure prediction.bioRxiv : the preprint server for biology · 2026Article
- Making Ends Meet or Just Meeting at the Ends? Assessing End-to-End Distance in Folded RNA Sequences and Other Branched Structures.Bulletin of mathematical biology · 2026Article
- Exploring Secondary Structure Predictions for RNA-Targeted Drug Discovery: Power and Challenges.Journal of chemical information and modeling · 2026Article
- An efficient algorithm for exploring RNA branching conformations under the nearest-neighbor thermodynamic model.Algorithms for molecular biology : AMB · 2026Article
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- Exploring the Boundaries of Modern Quantum Annealers with RNA Structure Prediction.The journal of physical chemistry. B · 2026Article
- Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning.Briefings in bioinformatics · 2026Article
- Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods.bioRxiv : the preprint server for biology · 2025Article
- Nucleotide dependency analysis of genomic language models detects functional elements.Nature genetics · 2025Article
- Can geometric combinatorics improve RNA branching predictions?BMC bioinformatics · 2025Article
- ECSFinder: optimized prediction of evolutionarily conserved RNA secondary structures from genome sequences.Nucleic acids research · 2025Article
- AlignmentFold and AlignmentPartition: Improving the align-then-fold approach for RNA secondary structure prediction.bioRxiv : the preprint server for biology · 2025Article
- RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks.Nature communications · 2025Article
- Comprehensive benchmarking of large language models for RNA secondary structure prediction.Briefings in bioinformatics · 2025Article
- IPANEMAP Suite: a pipeline for probing-informed RNA structure modeling.NAR genomics and bioinformatics · 2025Article
- Robust RNA secondary structure prediction with a mixture of deep learning and physics-based experts.Biology methods & protocols · 2025Article
- Analyzing aptamer structure and interactions: in silico modelling and instrumental methods.Biophysical reviews · 2024Review
- DecoyFinder: Identification of Contaminants in Sets of Homologous RNA Sequences.bioRxiv : the preprint server for biology · 2024Article
- Intronic RNA secondary structural information captured for the humanNAR genomics and bioinformatics · 2024Article
- RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction.Journal of molecular biology · 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
1 author.
Funding
Abstract
RNA secondary structure prediction is widely used. As new methods are developed, these are often benchmarked for accuracy against existing methods. This review discusses good practices for performing these benchmarks, including the choice of benchmarking structures, metrics to quantify accuracy, the importance of allowing flexibility for pairs in the accepted structure, and the importance of statistical testing for significance.
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.