RNA Secondary Structure Prediction Based on Energy Models

Manato Akiyama, Kengo Sato

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter introduces the RNA secondary structure prediction based on the nearest neighbor energy model, which is one of the most popular architectures of modeling RNA secondary structure without pseudoknots. We discuss the parameterization and the parameter determination by experimental and machine learning-based approaches as well as an integrated approach that compensates each other’s shortcomings. Then, folding algorithms for the minimum free energy and the maximum expected accuracy using the dynamic programming technique are introduced. Finally, we compare the prediction accuracy of the method described so far with benchmark datasets.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages89-105
Number of pages17
DOIs
Publication statusPublished - 2023

Publication series

NameMethods in Molecular Biology
Volume2586
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • Machine learning
  • Maximum expected accuracy
  • Minimum free energy
  • Nearest neighbor model
  • RNA secondary structure prediction
  • Thermodynamic parameters

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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