Applying length-dependent stochastic context-free grammars to RNA secondary structure prediction
type: Item
instance of: scholarly article
title: Applying length-dependent stochastic context-free grammars to RNA secondary structure prediction
zbMATH Open document ID: 1461.68100
author: Frank Weinberg
author: Markus E. Nebel
published in: Algorithms
publication date: 2019-03-26T00:00:00Z
review text: Summary: In order to be able to capture effects from co-transcriptional folding, we extend stochastic context-free grammars such that the probability of applying a rule can depend on the length of the subword that is eventually generated from the symbols introduced by the rule, and we show that existing algorithms for training and for determining the most probable parse tree can easily be adapted to the extended model without losses in performance. Furthermore, we show that the extended model is suited to improve the quality of predictions of RNA secondary structures. The extended model may also be applied to other fields where stochastic context-free grammars are used like natural language processing. Additionally some interesting questions in the field of formal languages arise from it.
Mathematics Subject Classification ID: 68Q42
Mathematics Subject Classification ID: 92D20
zbMATH DE Number: 7042106
zbMATH Keywords: stochastic context-free grammar
zbMATH Keywords: length-dependency
zbMATH Keywords: RNA secondary structure prediction
describes a project that uses: KineFold
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/a4040223
OpenAlex ID: W2097508084
cites work: Algorithms for Loop Matchings
cites work: Q4198075
cites work: Biological Sequence Analysis
DOI: 10.3390/A4040223
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description: scientific article; zbMATH DE number 7042106