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April 18, 2026BioinformaticsOpen Access

Spark: Sparse Hierarchical Energy Minimization for Scalable Prediction of RNA Pseudoknots

Mateo Gray Sebastian Will Hosna Jabbari

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Key Points

The aim is to develop a scalable method for predicting RNA pseudoknots using energy minimization techniques.
Utilized sparse hierarchical energy minimization algorithms.
Focused on RNA sequences and their structural predictions.
Analyzed performance against standard benchmarks.
Achieved significant improvements in prediction accuracy.
Demonstrated scalability for large RNA datasets.
Outperformed existing methods in various test scenarios.

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Implication

This research demonstrates effective RNA pseudoknot prediction methods, suggesting scalable solutions in bioinformatics.

Abstract

Supplementary data are available at Bioinformatics online.

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Cite This Study

Gray et al. (2026) studied this question.

synapsesocial.com/papers/69eb0ac4553a5433e34b4ae3 https://doi.org/10.1093/bioinformatics/btag194

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1SparseRNAfolD: optimized sparse RNA pseudoknot-free folding with dangle consideration2024 · 5 citations
2Algorithmic Topology of RNA Secondary Structures: Free-Energy Minimization and Pseudoknot Prediction in Genomic SequencesOriginal Research · 2026
3Hierarchical Analysis of RNA Secondary Structures with Pseudoknots Based on Sections2025
4Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials2024 · 26 citations
5Prediction of RNA Structure Using Genetic Approach2024

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