Algorithms for ranking and unranking the combinatorial set of RNA secondary structures

In this paper, we study the combinatorial set of RNA secondary structures. The main contribution of this paper is the developed ranking and unranking algorithms for the combinatorial set of pseudoknot-free RNA secondary structures of length [Formula: see text] with [Formula: see text] base-pairs. The developed algorithms are based on a bijection between the set of RNA structures and the variants of the corresponding AND/OR tree structure. Assuming a unit-cost RAM model, the developed algorithms have polynomial time complexity [Formula: see text] for [Formula: see text] and [Formula: see text] for [Formula: see text]. In contrast to the existing algorithms, the new algorithms do not require preprocessing steps and have better overall time complexity.

Authors

Institutions

Publication Details

Journal
Discrete Mathematics Algorithms and Applications
Published
2025-03-24
DOI
https://doi.org/10.1142/s1793830925500594
Citations
1
Primary Topic
RNA and protein synthesis mechanisms
Type
article
Field-Weighted Citation Impact
0.81
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Algorithms for ranking and unranking the combinatorial set of RNA secondary structures

Yuriy Shablya, Dmitry Kruchinin
1 citations
Discrete Mathematics Algorithms and Applications
RNA and protein synthesis mechanisms
0.81
article

Algorithms for ranking and unranking the combinatorial set of RNA secondary structures

Yuriy Shablya, Dmitry Kruchinin
article en
1 citations

Abstract

In this paper, we study the combinatorial set of RNA secondary structures. The main contribution of this paper is the developed ranking and unranking algorithms for the combinatorial set of pseudoknot-free RNA secondary structures of length [Formula: see text] with [Formula: see text] base-pairs. The developed algorithms are based on a bijection between the set of RNA structures and the variants of the corresponding AND/OR tree structure. Assuming a unit-cost RAM model, the developed algorithms have polynomial time complexity [Formula: see text] for [Formula: see text] and [Formula: see text] for [Formula: see text]. In contrast to the existing algorithms, the new algorithms do not require preprocessing steps and have better overall time complexity.

Discrete Mathematics Algorithms and Applications
Tomsk State University of Control Systems and Radio-Electronics (RU), Sirius University of Science and Technology (RU)
Openalex Percentile: Top 30%
RNA and protein synthesis mechanisms
0.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.